Extinction Dynamics Model TRAnsparent and Comprehensive Evaludation
Ludmilla Figueiredo
This is the “TRAnsparent and Comprehensive model Evaludation” document (TRACE) for the "Extinction dynamics model". Following the principles devised by @schmolkeecological2010, it uses the terminology and document structure presented by @grimmtowards2014 and @augusiakmerging2014. It is organized as a computational notebook to facilitate reproducibility by documenting all analytical process in a transparent fashion (Borregaard & Hart 2016). This documentation is available in the form of code chunks, which can be accessed but are not necessary for comprehension of the contents of the notebook. Figures and tables that are cross-referenced in the main text "Extinction dynamics under multiple perturbations" or inside the TRACE itself are included in the sub-section headers they belong, to facilitate navigation.
## Data processing
library(tidyverse);
library(reshape2);
library(lubridate)
## Data visualization
library(viridis);
library(ggcorrplot);
library(corrplot);
library(RColorBrewer);
library(cowplot);
theme_set(theme_minimal())
## Data analysis
library(FactoMineR);
library(factoextra);
library(missMDA);
library(sp);
library(raster);
## Data source
library(rgbif);
## customize facet names used to id seed sizes
sizes_facets <- c("Small (1g)", "Medium (2g)", "Big (5g)")
names(sizes_facets) <- c("0.0001", "0.0003", "0.001")
# Formatting and cross-referencing tables and figures
library(knitr)
library(kableExtra)
library(stringr)
## set-up directories
source("../../../set_dirs.R")
## set-up graphical theme
source(file.path(EDMdir,"theme_edm.R"))
###########################################################################################
## Data for parametrization of the model ##
###########################################################################################
## Pre-processing of LEDA files: metadata was taken away;
## white-spaces were replace with underscores;
## and brackets and other special caracteres ("/", "^") were deleted.
# List of species used for parameterization (further details in Appendixes)
spp_list <- read_csv(file.path(traitsdir, "spp_list.txt"), col_names = TRUE);
dispersal_leda <- read_delim(file.path(traitsdir,"LEDA/dispersal_type_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name,
mode_leda = dispersal_type,
vector_leda = dispersal_vector,
gen_vector_leda = gen._dispersal_vector,
gen_type_leda = gen._dispersal_type)
seedmass_leda <- read_delim(file.path(traitsdir,"LEDA/seed_mass_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name,
diaspore = diaspore_type,
single_value = single_value_mg,
mean_sm = mean_SM_mg,
min_sm = minimum_SM_mg,
max_sm = maximum_SM_mg)
seednumber_leda <- read_delim(file.path(traitsdir,"LEDA/seed_number_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name,
repro_unit = reproduction_unit_measured,
single_value,
mean_number = average_SNP)
lifespan_leda <- read_delim(file.path(traitsdir,"LEDA/plant_life_span_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name, leda_span = plant_lifespan)
lifespan_biolflor <- read_csv(file.path(traitsdir,"lifespan_biolflor.csv"),
col_names = TRUE)
clonal_leda <- read_delim(file.path(traitsdir,"LEDA/clonal_growth_organs_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name,
organ1 = clonal_growth_organ_1,
organ2 = clonal_growth_organ_2,
organ3 = clonal_growth_organ_3,
organ4 = clonal_growth_organ_4)
firstflower_leda <- read_delim(file.path(traitsdir,
"LEDA/age_of_first_flowering_clean.txt"),
delim = ";", col_names = TRUE)%>%
dplyr::select(sp = SBS_name,
first_flower = age_of_first_flowering,
min_firstflower = Minumum_years,
max_fistflower = Maximum_years)
seedbank_leda <- read_delim(file.path(traitsdir,"LEDA/seed_bank_clean.txt"),
delim = ";", col_names = TRUE,
col_types = cols(
.default = col_double(),
general_method = col_character(),
SBS_name = col_character(),
seed_bank_type = col_character(),
seed_bank_method = col_character(),
max_possible_longevity = col_character(),
layer_distribution = col_character(),
reference = col_character(),
area_unit = col_logical(),
area_expressed = col_number(),
seed_bank_present = col_logical(),
vegetation_present = col_logical(),
last_occurrence = col_character()))%>%
dplyr::select(sp = SBS_name, duration = seed_bank_type)
seedlongevity_leda <- read_delim(file.path(traitsdir,"LEDA/seed_longevity_clean.txt"),
delim = ";", col_names = TRUE,
col_types = cols(
SBS_number = col_double(),
SBS_name = col_character(),
general_method = col_character(),
Trial_number = col_double(),
seed_bank_type = col_character(),
max_longevity = col_double(),
max_possible_longevity = col_character(),
valid = col_double(),
SSB_seed_longevity_index = col_double(),
reference = col_character(),
seed_bank_method = col_character(),
original_reference = col_character()
))%>%
dplyr::select(sp = SBS_name, longevity = seed_bank_type)
## Values of minimal and maximal sizes (mass) for 47 species (Weiss et al. 2013)
weisstraits <- read_delim(file.path(traitsdir,"Weiss_etal_2013_OIKOS00957",
"weissetal_appendix3_spplist_clean.txt"),
delim = ";", col_names = TRUE,
col_types = cols(
Species = col_character(),
Maximum_plant_mass = col_character(),
Growth_form = col_character(),
Resource_response = col_character(),
Grazing_response = col_character(),
Clonality = col_character(),
Lateral_spread = col_character(),
Integration = col_character(),
PFT_ID_ = col_character()))%>%
dplyr::select(sp = Species, massgroup = Maximum_plant_mass)%>%
mutate(sp = str_remove(sp, fixed("_L.")))
### table defining groups of size, used for sorting species
weiss_sizegroups <- tibble(massgroup = c("large","medium","small"),
weiss_maxmass = c(5,2,1),
weiss_seedmass = c(0.001,0.0003,0.0001))
### filtered species from spp_list that have been
weiss_goetmasses <- inner_join(weisstraits, weiss_sizegroups)%>%
inner_join(., spp_list)
## Flower phenology
flowerphenology_biolflor <- read_csv(file.path(traitsdir, "biolflor_phenology.csv"),
col_names = TRUE,
col_types = list(col_character(),
col_character(),
col_integer(),
col_integer(),
col_integer(),
col_integer()))
## Species dispersal kernels
dispersal_ve <- read_csv(file.path(traitsdir, "dispersaldistance_vittozengler_2007.txt"),
col_names = TRUE,
col_types = cols(
sp = col_character(),
max_dist_ve = col_character(),
disp_mode_ve = col_character(),
genus_max_dist_ve = col_double(),
genus_mode_ve = col_character()
))
## Time-series of temperature in Göttingen
DWDdata <- read_delim("temperature_series/produkt_klima_monat_18570101_20171231_01691.txt",
col_names = TRUE,
delim = ";",
col_types = cols(
STATIONS_ID = col_character(),
MESS_DATUM_BEGINN = col_double(),
MESS_DATUM_ENDE = col_double(),
QN_4 = col_character(),
MO_N = col_character(),
MO_TT = col_character(),
MO_TX = col_character(),
MO_TN = col_character(),
MO_FK = col_double(),
MX_TX = col_double(),
MX_FX = col_double(),
MX_TN = col_double(),
MO_SD_S = col_double(),
QN_6 = col_character(),
MO_RR = col_character(),
MX_RS = col_double())) %>%
dplyr::select(MESS_DATUM_BEGINN,
MESS_DATUM_ENDE,
monthT = MO_TT)
tempseries_goettingen <- read_csv("temperature_series/temperaturegoettingen_18571917.csv",
col_names = TRUE,
col_types = cols(
week = col_double(),
meantemp = col_double(),
begin_date = col_date(format = ""),
week_year = col_character(),
week_inyear = col_double()
)) %>%
mutate(meantemp_K = meantemp + 273.15)
## Pollination vectors
pollination <- read_csv(file.path(traitsdir, "biolflor_pollination.csv"),
col_names = TRUE,
cols(species = col_character(),
pollen_vector = col_character(),
self_proba = col_double(),
selfing_at_failoutcross = col_logical()))
## Base values of germination and mortality rates of grassland plant species
marba <- read_csv(file.path(traitsdir, "birth_death_marba.csv"),
col_names = TRUE,
cols(
sp = col_character(),
mortalityrate = col_double(),
birthrate = col_double(),
temp = col_double()
))
## bioclimatic data
bioclim_edm.df <- read_csv(file.path(traitsdir, "bioclim_edm.csv"),
col_names = TRUE,
col_types = cols(species = col_character(),
temp_opt = col_double(),
tmean_sd = col_double(),
tmin_min = col_double(),
tmax_max = col_double(),
temp_tol = col_double()))
## Data frames for validation
edm_traits <- read_csv(file.path(EDMdocsdir, "spp_traits" ,"EDM_spptraits.csv"),
col_names = TRUE)
###########################################################################################
## Internal objects ##
###########################################################################################
## italics of species names
mk_itspp <-function(df){
if("sp" %in% names(df)){
df <- rename(df, species = sp)}
df %>%
mutate(species = str_replace(species, pattern = "_", replacement = " ")) %>%
mutate(species = paste0("*", species, "*"))
}
## Constants and base value of the Metabolic Theory of Ecology
aE <- 0.63 # activation energy
Boltz <- 8.62*(10^(-5)) # Boltzmann constant
## global parameters
odd_globalpars.df <- read_csv(file = file.path(EDMdocsdir, "tables/odd_globalpars.csv"),
col_names = TRUE,
col_types = cols(.default = col_character()))
## table of dispersal kernels
kernels.df <- read_csv("tables/dispersal_kernels.csv", col_names = TRUE,
col_types = cols(.default = col_character()))
## table of state variables of the model
state_vars.df <- read_csv(file = file.path(EDMdocsdir, "tables/odd_statevars.csv"),
col_types = cols(.default = col_character()))
disp_groups.df <- read_csv(file = file.path("tables/dispersal_groups.csv"),
col_names = TRUE,
col_types = cols(.default = col_character()))
###########################################################################################
## Dataframes for validation ##
###########################################################################################
dens_raw.df <- read_csv(file.path(EDMdocsdir, "validation/vegoekol2Quadrate_copy.csv"),
col_names = TRUE, col_types = cols(.default = col_double(),
Name = col_character())) %>%
mutate_at(vars(-Name),funs(replace_na(., 0))) %>%
rename(fragment = Name)## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.denssumm_raw.df <- read_csv(file.path(EDMdocsdir,
"validation/vegoekol2Quadrate_summ_copy.csv"),
col_names = TRUE,
col_types = cols(.default = col_double(),
Name = col_character())) %>%
rename(fragment = Name)
# traitspace from simulated communities
scen_wgtmean.df <- read_csv(file.path(semiproductsdir, "masswgttraits.csv"),
col_names = TRUE)
### function for trait analyses in "Validation"
get_pcaoutputs <- function(tab4pca, traitnames.dict){
res.pca <- PCA(tab4pca, scale.unit = TRUE, graph = FALSE)
scree.plot <- fviz_eig(res.pca, ncp = 5, addlabels = TRUE,
barcol = "#CFaFD6", barfill = "#CFaFD6")
pca.inds <- get_pca_ind(res.pca)
## Principal Component Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the variables"
## 2 "$cor" "Correlations between variables and dimensions"
## 3 "$cos2" "Cos2 for the variables" = quality of representation in the factor map
## 4 "$contrib" "contributions of the variables" (in percentage)
pca.vars <- get_pca_var(res.pca)
## name variables with more identifiable names
dimnames(pca.vars$coord)[[1]] <- traitnames.dict[dimnames(pca.vars$coord)[[1]]]
pca.vars$coord <- pca.vars$coord[order(rownames(pca.vars$coord)), order(colnames(pca.vars$coord))]
dimnames(res.pca$var$cos2)[[1]] <- traitnames.dict[dimnames(res.pca$var$cos2)[[1]]]
res.pca$var$cos2 <- res.pca$var$cos2[order(rownames(res.pca$var$cos2)), order(colnames(res.pca$var$cos2))]
cos2vars.plot <- fviz_pca_var(res.pca, col.var = "cos2", title = "Quality of representation of variables",
reppel = TRUE) +
theme_edm() +
theme(legend.position = "right")
dimnames(res.pca$var$contrib)[[1]] <- traitnames.dict[dimnames(res.pca$var$contrib)[[1]]]
res.pca$var$contrib <- res.pca$var$contrib[order(rownames(res.pca$var$contrib)),
order(colnames(res.pca$var$contrib))]
contribvars.plot <- fviz_pca_var(res.pca, col.var = "contrib", reppel = TRUE,
title = "Variables contributions to PCs") +
theme_edm() +
theme(legend.position = "right")
dimnames(pca.vars$cor)[[1]] <- traitnames.dict[dimnames(pca.vars$cor)[[1]]]
pca.vars$cor <- pca.vars$cor[order(rownames(pca.vars$cor)), order(colnames(pca.vars$cor))]
corrvars.plot <- ggcorrplot(pca.vars$cor,
colors = c("#FDE725FF", "#f7f7f7", "#440154FF")) +
## extremes taken from viridis(n = 2, option = "D")
theme_edm() +
theme(axis.title = element_blank(),
axis.text.x = element_text(angle = 70, hjust = 1),
axis.ticks = element_blank())
dimnames(res.pca$var$coord)[[1]] <- traitnames.dict[dimnames(res.pca$var$coord)[[1]]]
res.pca$var$coord <- res.pca$var$coord[order(rownames(res.pca$var$coord)),
order(colnames(res.pca$var$coord))]
contribvars.barplot <- fviz_contrib(res.pca, choice = "var", axes = 1:2, color = "grey77") +
theme_edm() +
labs(x = "Variable") +
theme(axis.text.x = element_text(angle = 75, hjust = 1))
## diagnostic plot
scree_corr.plots <- plot_grid(plot_grid(corrvars.plot,scree.plot,
ncol = 2, rel_widths = c(3,1), labels = c("A", "B")),
plot_grid(contribvars.plot, cos2vars.plot,
ncol = 2, labels = c("C", "D")),
nrow = 2, rel_heights = c(1,1.5))
return(list(pca = res.pca, scree.plot = scree.plot, pca.vars = pca.vars, pca.inds = pca.inds,
cos2vars.plot = cos2vars.plot, contribvars.plot = contribvars.plot,
corrvars.plot = corrvars.plot, contribvars.barplot = contribvars.barplot,
scree_corr.plots = scree_corr.plots))
}
traitnames.dict <- list(bankduration = "Seedbank duration",
clonality_TRUE = "Reproduction: clonal",
clonality_FALSE = "Reproduction: not clonal",
compartsize = "Biomass: allocation compart. size",
firstflower = "Flowering: start age",
floron = "Flowering: begin",
floroff = "Flowering: end",
kernel_long = "Dispersal: long",
kernel_medium = "Dispersal: medium",
kernel_short = "Dispersal: short",
'kernel_short-long' = "Dispersal: short/long",
'kernel_short-medium' = "Dipersal: short/medium",
'kernel_medium-long' = "Dipersal: medium/long",
'kernel_long-medium-short' = "Dispersal: all kernels",
pollen_vector_wind = "Reproduction: wind poll.",
pollen_vector_insects = "Reproduction: insect poll.",
'pollen_vector_wind-insects' = "Reproduction: wind/insect poll.",
self_failoutcross_TRUE = "Reproduction: selfing (failed outcross)",
self_failoutcross_FALSE = "Reproduction: no selfing",
self_proba = "Reproduction: probability of selfing",
seedmass = "Biomass: seed size",
seednumber = "Seed number",
seedon = "Seed release: begin",
seedoff = "Seed release: end",
span = "Lifespan")1 Problem formulation
Summary
When an ecosystem undergoes a perturbation, species extinctions are not necessarily immediate. Extreme events excluded, e.g. a volcanic island explosion, when species might be immediately extirpated @quammensong1996, in most cases, it can take up to many decades for most extinctions to happen @figueiredounderstanding2019, because species have different capabilities to survive in disturbed conditions. Metapopulation dynamics and resistance traits grant such capabilities [@saarwhich2012, @saartrait2017]. The number of such extinctions, which are bound to happen in the future is known as the “extinction debt” [@tilmanhabitat1994, @kuussaariextinction2009]. The it takes for these extinction debts to happen is known as the "relaxation time" @diamondbiogeographic1972. This concept implies that, while these extinctions have not happened, i.e. the debt has not been paid by the ecosystem, extinctions can also be both accelerated and delayed by eco-evolutionary processes happening at different levels of the community (i.e., individual, populational, and community). Moreover, conservation measures could be planned to avoid them. To elucidate the relative importance of these processes, we build an individual-based model to simulate and characterize community response to different kinds of habitat perturbation at different ecological levels.
1.1 Motivation
The model is designed to elucidate how ecological processes in a plant-insect community are affected by ecosystem disturbance, and how they affect extinction dynamics. More specifically, we are interested at how species dispersal capacity and reproductive strategy, for example, delay or accelerate species extinctions.
1.2 Questions
With this model, we aim at answering the following questions:
- How does the regime of disturbance affects extinction debt and relaxation time?
Abiotic perturbations cause biotic perturbations (e.g., pollination loss) as a secondary effect. The secondary perturbations amplify the effects of the primary disturbances alone. Thus, predictions of extinction debts and relaxation time that only consider isolated abiotic perturbations yield underestimations.
- Which traits allow species to survive through the payment of the debt?
Following disturbance, initial species survival depends on the severity of the extinction-causing disturbance, and on the species abundance. During the relaxation process, community reassembly and microevolution affect are the main factors for species survival. Both processes either arise from (community reassembly) or affect (microevolution) species trait spaces. Our objective is, thus, to describe the change in community trait space during the relaxation process.
1.3 Use and applicability
The model is suited for theoretical explorations of eco-evolutionary dynamics.
2 Model description
Summary: This is a complete version of the model description presented in @figueiredoinprep.
2.1 Overview
2.1.1 Purpose
The model simulates the effects of abiotic and biotic perturbations on i) plant species richness and population dynamics, and ii) community trait space.
2.1.2 Entities, state variables and scales (Table S@ref(tab:statevars))
The model simulates the life cycle of plants in a landscape. Plant individuals are characterized by state variables and species traits (Table @ref(tab:statevars), which are used as parameters values in the simulation of the individual’s life cycle (detailed in the next section, Process overview and scheduling).
The landscape is represented as a grid of cells. Each grid cell has an area of 1 m² and represents either suitable or unsuitable habitat (Table @ref(tab:statevars)). There is no upper limit on the size of landscape (minimal size would be 1 m²), but increasing the size of the landscape allows more individuals to occupy and thus, can be decrease the speed of simulations. The configuration of these cells in the grid is defined by the input, thus, by the experimental design. Temperature is a global variable in the model (all patches are subjected to the same temperature). The temperature is used to calculate the biological rates of the organisms being simulated (germination, growth, and mortality, each detailed in the Submodels section). The model runs on weekly discrete time-steps to account for the seasonality of management (mowing) and species phenology. The species to be included in the simulation, the landscape configuration, the regime of perturbation (type of perturbation, time of occurrence, and magnitude), and total duration of the simulation are determined by the user-defined experimental design (details in the Input and Disturbance submodel sections).
state_vars.df%>%
knitr::kable(caption = "Entities and their respective state variables in the model. Variables marked with (*) can evolve as described in the Microevolution submodel. Species specific parameters must be provided as input. The criteria regarding the selection and attribution of the respective values is described in the sections listed below.",
longtable = TRUE, booktabs = TRUE) %>%
collapse_rows(columns = , valign = "top") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) ## %>%| Entity | State variable (unit) | Scope |
|---|---|---|
| Plant | Species | Species name |
| Developmental stage | Temporary status | |
| Age(weeks) | ||
| Vegetative biomass of leaves (g) | ||
| Vegetative biomass of stem (g) | ||
| Vegetative biomass of roots (g) | ||
| Reproductive biomass (g) | ||
| Pollination status | ||
| Life span (weeks)* | Species specific parameter | |
| Age of first flowering (weeks)* | ||
| Beginning of flowering season (week of year) | ||
| End of flowering season (week of year) | ||
| Pollination vector | ||
| Beginning of sowing season (week of year) | ||
| End of sowing season (week of year) | ||
| Dispersal kernel | ||
| Clonal ability | ||
| Seed mass (g) | ||
| Capacity of selfing at fail of outcrossing | ||
| Probability of selfing upon pollination | ||
| Maximal number of seeds (weeks)* | ||
| Seed-bank duration (weeks)* | ||
| Temperature optimum | ||
| Temperature tolerance | ||
| Germination proportionality constant \((week^{-1})\) | ||
| Mortality proportionality constant \((week^{-1})\) | ||
| Biomass growth proportionality constant \((g/week^{-1})\) | ||
| Landscape grid cell | Habitat suitability | Experimental design |
## kable_styling(full_width = FALSE) %>%
## column_spec(1, width = "1.5cm") %>%
## column_spec(2, width = "10.5cm") %>%
## column_spec(3, width = "9cm")2.1.3 Process overview and scheduling (Figure S@ref(fig:flowchart))
At each time-step, each individual goes through its life cycle processes, which vary according to the life history strategy determined by the individual's traits and current developmental stage (Table @ref(tab:statevars)), i.e., seeds, juveniles and adults go through different processes (Fig. @ref(fig:flowchart)).
knitr::include_graphics(file.path(EDMdocsdir,"figures/model_flowchart.png"))
Flowchart of the processes simulated by the model for each individual plant. The biological processes differ between individuals according to state variables such as stage (as identified by the line type), or phenology (e.g.flowering season). The circumstances where state variables control the outcome of certain biological processes, namely pollination, maturation, germination, or excesive growth (\(biomass_{cell} > K_{cell}\) refers to the biomass production being over the landscape carrying capacity) can also determine the course of an individual's life cycle.
The processes simulated in the model are the following (scheduling is depicted in Fig. @ref(fig:flowchart), and further details are described in Details section):
Management: Annually, the effects of management (mowing or grazing) are simulated as the reduction of above-ground biomass of juvenile and adult plants.
Biomass growth and resource allocation: Whole organism biomass production is calculated according to metabolic theory of ecology (MTE, [@ernestthermodynamic2003; @browntowards2004]). The total production is allocated to vegetative or reproductive structures, according to the individual's developmental stage and phenology. Juveniles and non-reproducing adults can only accumulate vegetative biomass, which is equally divided among "root", "stem", and "leaves" organs. During their species-specific reproductive season, determined by the phenological traits of start and end of flowering season, adults allocate biomass production to reproductive structures if they have reached a species-specific minimal vegetative biomass.
Maturation of juveniles: Juvenile individuals become adults once they reach their age of first flowering, a species-specific phenological trait.
Mortality: The probability of density-independent mortality is calculated for each individual, according to the metabolic theory of ecology (@browntowards2004). Density-dependent mortality is calculated once total vegetative standing biomass production surpasses the carrying capacity. Individuals die acccording to the species local (at the grid cell level) relative adaptation to temperature (fitness).
Pollination is implicitly simulated as pollination service, i.e., a plant is to sexually produce offspring only if it has been pollinated. Plants can be pollinated if they have enough reproductive biomass to produce at least one seed. The number of individuals being pollinated depends upon the number of individuals visited, pollination efficiency of the vector, and on the regime of pollination, all defined by the experimental design.
Sexual reproduction happens for individuals that have been pollinated. Trait inheritance is calculated for seeds thus produced (described below). Species also have a species-specific probability of selfing, in which case, no trait inheritance is calculated. The number of seeds produced depends on the species-specific seed weight and on the amount of reproductive biomass of the mother plant, and it is limited to species-specific values of maximal weekly seed production.
Asexual reproduction is simulated for clonal plants that do not get pollinated. Clonality is a species-specific trait. Probability of clone production is a global parameter (Table @ref(tab:globalpars)), and at most one clone is produced per cloning plant. Clones are initialized as juveniles, in the same location as the plant generating it.
Trait inheritance is simulated upon sexual reproduction through a simplified model of phenotypical change of a population under panmixia. Clones and seeds produced through selfing are exact copies of the plants that produced them.
Seed dispersal: Seeds can be dispersed at short (0.1-100 m), medium- (100-500 m), and long-distances (1-10km), accoding to the species-specific dispersal parameters. The phenology of seed release is also a species-specific trait (Table @ref(tab:statevars)).
Seed germination is only possible in grid-cells labelled "suitable". Seeds that fall outside the landscape or in unsuitable grid-cells are considered dead and discarded from the simulation. The probability of seed germination is calculated for each individual, according to the MTE. If a seed is successful, it turns into a juvenile.
Shedding: At the end of the flowering season, adult plants lose all their reproductive biomass.
Winter dieback: At the last week of the year, adults lose all of the biomass allocated to leaves, and 50% of the biomass allocated to stem.
Disturbances can happen as temperature change, loss of habitat area, fragmentation, and loss of pollination. Temperature change is simulated if the obligatory the input of a temperature time series depicts the change. Loss of habitat area is simulated by the input of a proportion of habitat to be marked as "unsuitable" or by providing a raster file with the new habitat configuration, and the frequency of change, i.e. the time steps when the change happens. Fragmentation is only simulated through raster file(s) of the new habitat configuration. Loss of pollination is simulated by an input defining the intensity (\((0,1]\)), the frequency, and the specificity (i.e. which species are affected) of loss.
2.1.4 Table S@ref(tab:globalpars)
odd_globalpars.df %>%
knitr::kable(caption = "Global parameters of the model, their respective values, and references used to define them.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Global parameter | Value | References |
|---|---|---|
| Beginning of management season | \(31^{st} week\) (first week of August) | Kormann et al. (2015) |
| End of management season | \(39^{st}\) (last week of September) | Kormann et al. (2015) |
| Probability of selfing at fail of outcrossing | 50% | Arbitrary value |
| Allocation of reproductive biomass to seed production | 50% | Arbitrary value |
| Proportion of plants visited by pollinators | 1 | Arbitrary value |
| Efficiency of insect pollination | 60% | King et al. (2013) |
| Efficiency of wind pollination | 60% | Arbitrary value |
| Short-dispersal kernel | \(\mu = 1, \lambda = 0.2\) | Vittoz & Engler (2007), Bullock et al. (2017) |
| Medium-dispersal kernel | \(\mu = 0.2, \lambda = 3\) | Vittoz & Engler (2007), Bullock et al. (2017) |
| Short-dispersal kernel | \(\mu = 1000, \lambda = 100\) | Vittoz & Engler (2007), Bullock et al. (2017) |
| Boltzmann constant | 8.65-e5 -eV/K | Brown et al. (2004) |
| Activation energy | 0.63 -eV | Brown et al. (2004) |
| Landscape carrying capacity | 5T/ha | Bernhardt-Romermann et al. (2011) |
#kable_styling(full_width = FALSE)2.2 Design concepts
2.2.1 Basic design principles
The basic principle of the model is to capture how habitat change affects the individual, population and community levels of an ecosystem and how those processes contribute to the size of extinction debts and relaxation times. For its ability to scale individual level effects to community level patterns, the metabolic theory of ecology (MTE, @browntowards2004) was chosen as the guiding principle for calculating biological rates.
2.2.2 Emergence
Metapopulation and metacommunity dynamics arise from the local population dynamics and from species-specific dispersal abilities. Population dynamics results from individual biological demographic rates (biomass growth - both vegetative and reproductive biomass, germination, and mortality) that are calculated according to the MTE. Species coexistence arises from species density-dependent mortality depending on species relative adaptation to local temperature (fitness), where such limitation works as a local stabilizing mechanism. Competition arises from species relative fitness being updated locally, at every time step. Thus, extinct species leave open spaces that might be occupying by the surviving ones, according to their relative fitness.
2.2.3 Interactions
Organisms interact with the environment because all vital rates depend on local temperature, as determined by MTE. Plants also compete for space, since species local fitness limits its biomass growth.
2.2.4 Stochasticity
Reproduction, seed dispersal, germination and density-independent mortality are stochastic processes. The probability distribution used in each process is specified in their respective sub-model description. Mortality is specific for adult individuals older than their species lifespan, and for seeds falling in unsuitable habitat, or older than their seed bank duration. Density-dependent mortality is deterministic for small individuals of species going over their carrying capacity.
2.2.5 Observations
To keep track of extinction arising at different ecological levels (individual, population or community), the model outputs all state variables of each individual being simulated (Table @ref(tab:statevars)) at user-defined intervals. From these outputs, the model readily summarizes species diversity, population dynamics (abundance and struture), biomass allocation, and life-history events (at the community level, not for each species). Other analysis can be derived by the user with the same file.
2.3 Details
2.3.1 Initialization
Simulations are defined by the landscape configuration, the species pool, the climatic conditions (temperature) and the type of disturbance. Landscape configuration determines which grid cells constitute suitable habitat and can, therefore, be occupied. Initial temperature (temperature is a global variable of the model) is read from an input file and updated at every time step (the file contains a time-series of weekly temperatures). Individuals are created according to the list of species list given as input. Upon initialization, each individual is randomly placed in the landscape and receives an individual identification tag. The individual's grid location is recorded because it is relevant for the calculation of density-dependent mortality (detailed in the Density-dependent mortality section). The individual's trait values (\(v\)) are read from the species list input (Table @ref(tab:statevars)), except for values of seed number, seed bank duration, life span and age of first flowering. These values are highly variable (see Data evaluation section). Thus, the values for these traits are drawn from an Uniform distribution, \(v\sim\mathit{U}(v_{min}, v_{max})\), where \(v_{min}\) and \(v_{max}\) are the minimal and maximal trait values given in the species list input file. Individuals that are initialized at random stages ("seed", "juvenile", or "adult"). Juveniles have the same biomass as seeds, since they are considered young seedlings, with four weeks old. Adults are initialized with 75% of the species maximum adult biomass and have the age of first flower of the species.
2.3.2 Input
Simulation scenarios are user-defined, and most parameters are specified in input files. Table @ref(tab:parse_settings) lists such files, its contents, formatting, and how they should be parsed to the model. A simulations requires:
- Species list and trait values: The list of the species to be initialized, their respective trait values, and initial abundances (Table @ref(tab:statevars)). Details on the species and trait values data used to develop the model under Data evaluation.
- Initial landscape configuration: A raster file with binary values coding for habitat availability).
- Time series of temperature variation: temperature values (°C) to be assigned for all grid cells, for each week of simulation. The total duration of simulation is determined by the length of the time-series. Details on the time-series data used to develop the model are provided under Environmental characteristcs in Data evaluation.
Type, time and magnitude of environmental disturbance: A dedicated setting identifies the type of disturbance, if any, tp be simulated. Frequency and magnitude are provided in dedicated files:
- to simulate temperature change, the time series (item 3) should contain the temperature values to be used.
- to simulate area loss or fragmentation, the file must inform the time step(s) of change, and the respective proportion of area to be lost, or the raster file(s) containing the new configuration.
- to simulate pollination loss, the file must inform the regime of pollination loss, the time steps when it happens, and how much pollination remains available.
Details on the contents of these files and how they are implemented are available in the descriptions of each disturbance submodel).
2.3.3 Submodels
All biological rates in the model, namely biomass growth, germination, and (density-independent) mortality, are calculated according to the metabolic theory of ecology [@browntowards2004]: \[\begin{equation} B = b_{0}.m^\alpha.e^{\frac{-E}{k.T}} (\#eq:mte-B) \end{equation}\] where \(B\) is the metabolic rate, \(b_{0}\) is a taxon and stage-specific proportionality constant, \(m\) is the individual's body mass, \(\alpha\) is an allometric exponent, \(E\) is the activation energy, \(k\) is the Boltzmann constant, and \(T\) is the local temperature. Values of \(\alpha\), \(E\), and \(k\) are constants from the metabolic theory of ecology [@browntowards2004, @siblylife2012] Table @ref(tab:globalpars). The proportionality constant \(b_{0}\) is specifically parameterized for each process to generate (biologically) realistic rate values. We describe this parameterization in the Metabolic biological rates section of Data evaluation). Biological rates are converted into individual probabilities (of germination and mortality, only) as \[\begin{equation} p = 1 - e^{-B} (\#eq:mte-prob) \end{equation}\]2.3.3.1 Management
Annually, the effects of management (mowing or grazing) are simulated as the reduction of above-ground biomass of juvenile and adult plants. The regime of mowing, i.e. the period of the year when it occurs, the probability of occurence, and the maximum annual number of occurence, is defined by the experimental design (example described in the Management section of Data evaluation). Only individuals that have accumulated at least 50% of the species maximal vegetative biomass (biomass allocated to stems and leaves) have their vegetative reduced to 50% of its maximal values and lose all reproductive biomass.
2.3.3.2 Biomass growth & Resource allocation
An individual's biomass (\(m\)) is calculated as \[\begin{equation} m_{t+1}=m_t+g(t)\\ g(t)=B_{growth}\times(m_{max}-{m}_{t}) (\#eq:mass-growth) \end{equation}\]where \(g(t)\) calculates the total biomass production at a time-step \(t\), \(m_{max}\) refers to the species total maximal biomass (vegetative and reproductive), and \(B_{growth}\) is the rate of growth (Eq.@ref(eq:mte-B)). This growth function approaches the S-shaped Richards growth curve (@Richards 1959) parameterized according to the species minimal and maximal sizes, and age of first flowering (detailed in the Biomass growth rates section of Data evaluation).
The total production is allocated to vegetative or reproductive organs according to the allometric relationship between plant organs biomasses devised by @niklasenquistcanonical2002, to the individual's developmental stage, and to the species' reproductive phenology. The allometric relationship devised by @niklasenquistcanonical2002 states that standing leaf biomass scales as the 3/4-power of stem biomass and that stem and root biomasses scale isometrically in relation to each other, i.e. \(M_{leaf} ~ M_{stem}^{3/4} ~ M_{root}^{3/4}\). Moreover, the growth rates of the three biomass organs also scale isometrically, i.e. \(B_{leaf} ~ B_{stem} ~ B_{root}\). Thus, biomass growth is equally divided among all vegetative organs ("root", "stem", and "leaves") during vegetative growth. Juveniles and non-reproducing adults can only accumulate vegetative biomass. During their species-specific flowering season (determined by the phenological traits of start and end of flowering season Table @ref(tab:statevars)), adults allocate biomass production to the "reproductive" organs, if they have reached at least 50% of the species maximal size. Otherwise, they allocate to vegetative structures, as described above.
2.3.3.3 Maturation of juveniles
Juvenile individuals become adults once they reach their species-specific age of first flowering.
2.3.3.4 Mortality
Biomass growth and mortality are calculated in sequence, separately for adults and juveniles (Fig. @ref(fig:flowchart)). Only density-independent mortality is calculated for seeds.
Density-independent mortality is calculated for all individuals, except seeds that are still attached to the mother plant. Mortality rate, \(B_{mort}\), and probability, \(p_{mort}\), are calculated according to the MTE (Eq. @ref(eq:mte-B) and @ref(eq:mte-prob)). Individuals that are older than their maximal lifespan (always adults, see Maturation) are killed deterministically. Details on the parametrization of the normalization constant are described under the Mortality rates section of Data evaluation.
Density-dependent mortality is calculated once the total standing biomass in a grid-cell \[\begin{equation} M_{total} = \sum_{x=i}^{n_{cell}} m_i \end{equation}\] (\(n_{cell}\) being the total number of individuals in the cell) is higher than its carrying-capacity (\(K_{cell}\)), a global, constant value defined by the experimental design (an example is given in the Data evaluation section. Mortality is then simulated only for the species whose specific share of the total standing biomass production (\(M_{sp} = \sum_{x=i}^{n_{sp}} m\), \(n_{sp}\) being the number of individuals of the species) is above its relative carrying capacity (\(K_{cell_{sp}}\)). A species (\(K_{cell_{sp}}\)), i.e. its maximal standing biomass production inside a cell, depends on its temperature niche, defined by species-specific, non-evolving temperature optimum (\(T_{opt}\)) and tolerance values (\(T_{tol}\)). The temperature niche is modelled as a Gaussian function (\(f(T)\), with maximum value \({f}_{max} = f(T_{opt}) = 1\), and \(sd = T_{tol}\), \[\begin{equation} f(T) = {f}_{max} \times \exp(\frac{-(T-T_{opt})^2}{2 \times {T}_{tol}^2}) (\#eq:fitness) \end{equation}\]where \(T\) is the mean temperature calculated over the same time period as the optimum and tolerances were defined, which is the same time period over each fitness is updated. Species temperature niches are defined by the experimental design and an example of how we defined it is described in the respective section of the [Data evaluation][#species-temperature-niche].
A species relative carrying-capacity is then, \[\begin{equation} K_{cell_{sp}} = \frac{f_{sp}(T)}{\sum_{x=i}^{spp_{cell}} f_i(T)} \times K_{cell} \end{equation}\]where \(f_{sp}\) is the species absolute fitness value, and \(spp_{cell}\) are the species in the cell.
Thus, once \(M_{sp} > K_{cell_{sp}}\), individuals are ranked according to total biomass, and smaller (independent of stage, juvenile or adult) are killed first, until \(M_{sp} \le K_{cell_{sp}}\). Since the MTE predicts that smaller individuals have higher metabolic rates, we avoid the computational burden of drawing random processes (as it happens for density-independent mortality) by eliminating smaller individuals first.
2.3.3.5 Pollination
The occurrence of pollination is a stochastic process that depends on the availability of pollination service, which is determined by the experimental design and affects the total number of individuals that will produce offspring sexually.
The number of pollinated \(n_{poll}\) individuals is calculated (per species) from all the flowering plants with enough reproductive biomass to produce at least one seed (\(n_{repr}\)). Flowering plants are adult individuals that are in their reproductive season, a species specific trait determined (an example is decribed in Data evaluation), and the proportion of reproductive biomass available for seed production is determined by a global constant defined by the experimental design (Table @ref(tab:globalpars)). The number of effectively pollinated plants is drawn from a Binomial distribution \[\begin{equation} n_{poll} \sim\mathit{B}(n=n_{repr}\times{p}_{visit},p=p_{eff}) \end{equation}\]where \(p_{visit}\) is the proportion of visited flowers, and \(p_e\). Both are global constants in the model (Table @ref(tab:globalpars)). In the absence of disturbance, \(n_{poll}\) individuals of the species are identified as having been pollinated and can thus, produce seeds. Pollination disturbance decreases \(n_{poll}\) according to the pollination scenario, whose implementation is detailed in the Pollination loss submodel description.
2.3.3.6 Sexual reproduction
The number of seeds produced at each reproductive event is calculated as \(n{seeds} = m_{repr}/m_{seed}\), where \(m_{repr}\) is the amount of reproductive biomass and \(m_{seed}}\) is the species-specific seed weight. The amount is limited by the species weekly maximal number of seeds, i.e., even if the plant has enough biomass to produce more than its maximum, it will not. The species weekly maximal number of seeds is a trait value given as input to the model (an example is described in the Seed number section of Data evaluation). Species have a species-specific probability of selfing upon pollination. Moreover, some species can resort to selfing in case they are not pollinated. For such species, there is a global probability of success (Table @ref(tab:globalpars)). The trait values of the offspring are calculated according to a simplified model of phenotypical change of a population under panmixia (described below). After offspring production, the reproducing plant loses biomass from its reproductive compartment equivalent to the weight of all the seeds produced.
2.3.3.7 Asexual reproduction
A clonal plant that has not been pollinated has a global probability of generating a ramet (Table @ref(tab:globalpars)). The clone is initialized as a juvenile, in the same grid cell as the mother-plant, weighing 10% of the species total maximal biomass. Thus, clonality is a global strategy to assure seedling establishment .
2.3.3.8 Trait inheritance
Offspring inherits its trait values according to the type of reproduction it is generated from. For sexually, non-selfing, generated seeds, the new trait value \(v\) of evolvable traits (Table @ref(tab:statevars)) is calculated as \[\begin{equation} v=\frac{v_{mother}+v_{mother}}{2}+\mathit{N}(0,|\frac{(v_{mother}-v_{father})}{6}|) \end{equation}\]where \(v_{mother}\) is the trait value of the plant originating the seed and \(v_{father}\) is the trait value of a conspecific plant randomly chosen in the population. By using \((v_{mother}-v_{father})/6\), the introduced variability in trait hereditability is limited to the difference between the parent's values. The resulting trait value, however, is limited by the species minimal and maximal values, \(v_{min}\) and \(v_{max}\), given as input. Individuals generated through selfing and cloning inherent the same set of traits as the plant producing them.
2.3.3.9 Seed dispersal
During the reproductive season, seeds are produced, but not necessarily dispersed. Seed dispersal during species-specific seed release season. The model simulates kernel-based dispersal. Kernel parameters characterize the distribution of distances achieved by the species mode of dispersal.
Dispersal is simulated by drawing a random radian angle \(\theta=[0-2\times\pi\)) from Uniform distribution, and drawing the dispersal distance \(dist_{disp}\) from the species' dispersal kernel. The dispersal distance \(dist_{disp}\) is drawn from an Inverse Gaussian distribution parameterized according to the species-specific dispersal vector (an example is described in the Dispersal kernels. The new location is calculated as \begin{equation) (x_{new}, y_{new}) = (x+cos((dist_{disp},y+sin()_{disp}) \end{equation} where \((x,y)\) is the individual's current location (cartesian coordinates of the cell in the landscape). A species might have different dispersal kernels. Upon dispersal, one of them is randomly drawn from an Uniform probability distribution before drawing \(dist_{disp}\).
2.3.3.10 Seed germination
If a seeds is dispersed and falls on an unavailable grid cell, it dies immediately and is discarded from the simulation. Otherwise, its germination rate, \(B_{germ}\), and probability, \(p_{germ}\) are calculated according to the metabolic theory of ecology Details of the parameterization of normalization constant parameterized as described in the [Germination rate section]#germination-rate) of Data evaluation. The realization of germination is randomly drawn from a Bernoulli distribution, with probability \(p_{germ}\). If germination happens, the new seedling is identified as a juvenile individual. Seeds that do not germinate remain in the seed bank. Seeds that reach an age older than the species-specific seed bank duration are killed deterministically.
2.3.3.11 Disturbances
The model can simulate scenarios of temperature change, loss of habitat area (with or without fragmentation) and loss of pollination. These scenarios are defined independently from each other, which allows the combination of different perturbations into a single scenario. Each type of disturbance is simulated as follows:
2.3.3.11.1 Temperature change
The model is dependent on a temperature time series being input (to calculate the rates of biological processes), therefore, temperature change can be simulated by providing a time series that characterizes the temperature change for the duration of the simulation.
2.3.3.11.2 Area loss (without fragmentation)
The simulation scenario must define a proportion of habitat to be lost at a given time-step. Then, the number of contiguous grid cells equivalent to the area lost are marked as 'unsuitable' and all individuals occupying this cells are immediately killed. It is also possible to simulate area loss if a raster file describing a new configuration of habitat suitability of the original area is provided (associated with a time-step when it should be loaded).
2.3.3.11.3 Fragmentation
Habitat fragmentation can only be simulated via raster files. Similarly to area loss, cells are marked as unsuitable according to the description contained in the raster file and individuals in the area are killed. Several episodes of area loss or fragmentation can be simulated if several proportions or raster files describing loss are provided, associated with different time-steps. Individuals occupying cells that become "unsuitable" upon fragmentation are killed and discarded from the simulation.
We provide the createnlm.R script to generate raster files of a given area, using the NLMR package [@sciaininlmr2018] to . We describe its use in the Appendix.
2.3.3.11.4 Pollination loss
The regime of pollination loss is be defined according to its i) intensity, i.e., the proportion of flowers still being pollinated (\(p_{remain}\) = [0,1]), ii) frequency, i.e, the time-step(s) when loss happens (\(t_d\)), and iii) specificity, i.e., which species are affected. The intensity of loss affects pollinations by decreasing the number of individuals being identified as "pollinated", \(n_{poll}=n_{poll}\times{p}_{remain}\). The specificity of pollination loss can defined as "equal", "random", or "specific". In the scenario of "equal" pollination loss, all species are affected by \(p_{remain}\). Under the "random" scenario, \(p_{remain}\) is applied to the total individuals potentially reproducing, among all species. Thus, the number of pollinated individuals per species is randomly set. Under the "specific scenario", a given list of species loses pollination is affected by \(p_{remain}\). (see details in the Input section)
#5% the biomass allocated in reproductive compartment is used to produce seeds by plants that have been effectively pollinated (@weissgrazing2014).
#accounts for pollination efficiency (@Kingetal2013) reports 40% of visitors are not effective pollinators).3 Data evaluation
Summary: The model uses species trait values to control entities behaviour in the simulation of a plant's life cycle under given biotic (pollination services) and abiotic (temperature and habitat availability) conditions. Because this model was developed to study the community dynamics of temperate grasslands, inspired by @krausshabitat2010, we report how we retrieved, evaluated and adapted species trait values and temperature time series data to parameterize the model to mimick this ecosystem. The parameter values thus defined are used as model input. Therefore, users of the model can refer to this section when defining their own experiments. Data was retrieved from different literature sources and databases. Data that could not be obtained as such was imputed, as described in "Missing trait values" subsections. The final trait values chosen for each species are listed in [Appendix 4][#edm-spptraits].
3.1 Management
Management is implemented following the regime reported for Göttingen by @kormannlocal2015. Thus, at most once a year, between August and September, the effects of management (mowing or grazing) are simulated as described in the Model description.
3.2 Temperature time series (Figure S@ref(fig:dwd-tempseries))
Parametrization of metabolic rates has taken into account the monthly mean temperatures registered for the city of Göttingen, between 1857 and 2017 (@dwdcdc2018, station 1691). Monthly temperatures values where expanded to a weekly bases by assigning the same value to all weeks of the month (code available in the chunck below). The final temperature time series is depicted in Fig. @ref(fig:dwd-tempseries).
# According to the `DESCRIPTION_obsgermany file, MO_TT contains the monthly mean of daily temperature mean at 2m height.
dwd_toexpand <- DWDdata %>%
dplyr::select(MESS_DATUM_BEGINN,
MESS_DATUM_ENDE,
monthT)%>%
mutate(begin_date = ymd(MESS_DATUM_BEGINN),
end_date = ymd(MESS_DATUM_ENDE))%>%
dplyr::select(-c(MESS_DATUM_BEGINN, MESS_DATUM_ENDE))%>%
mutate(begin_week = week(begin_date),
end_week = week(end_date))%>%
dplyr::select(-c(end_date, end_week))%>%
rename(week_inyear = begin_week)%>%
mutate(week_year = paste(week(begin_date), year(begin_date), sep="-"))%>%
as_tibble()
dwd_expanded = dwd_toexpand #extract week-year and use it to expand, to detect months with 4 or 5 weeks
for(i in 1:1932){
for(j in 1:4){
new_date = dwd_toexpand$begin_date[i] + weeks(j)
new_weekyear = paste(week(new_date), year(new_date), sep="-")
if(!(new_weekyear %in% dwd_toexpand$week_year)){
dwd_expanded = add_row(dwd_expanded,
monthT = dwd_toexpand$monthT[i],
begin_date = new_date,
week_inyear = week(dwd_toexpand$begin_date[i] + weeks(j)),
week_year = new_weekyear)}
}
}
temperature_1857_2017 <- arrange(dwd_expanded, begin_date)%>%
mutate(week = row_number())%>%
dplyr::select(week, meantemp = monthT, begin_date, week_year, week_inyear)
write_csv(temperature_1857_2017,
"temperature_series/temperaturegoettingen_18571917.csv")ggplot(data = tempseries_goettingen, aes(x = begin_date, y = meantemp))+
geom_point(aes(colour=meantemp), size = 1.25)+
geom_line(aes(colour=meantemp))+
labs(x = "Year", y = "Temperature(°C)")+
theme_edm()+
theme(legend.position = "none")
Temperature time-series used in the first implementation of the model. Monthly mean temperatures registered for the city of Göttingen, between 1857 and 2017 (@dwdcdc2018, station 1691) were expanded to a weekly bases by assigning the same value to all weeks of a given month.
3.3 Species functional traits
Trait values used in the model Table @ref(tab:statevars) were retrieved from the LEDA Traitbase (@kleyerleda2008), the BiolFlor database [@klotzbiolflor2002], the FloraWeb database (http://www.floraweb.de), the WorldClim [@wordclim, @hijmansworldclim2005], and the GBIF database [@gbif2019]. The process of filtering and transformation of this data, as well as the parameterization based on it is described below.
3.3.1 Dispersal kernels (Table S@ref(tab:kernels))
Species dispersal types were retrieved from the LEDA database [@kleyerleda2008]. We used a classification of dispersal modes with similar distances [@vittozseed2007] to assign kernel parameters to the species. We reorganized the seven dispersal types originally devised by @vittozseed2007 into three super-types: short-, medium-, and long-distance dispersal @ref(tab:disp-groups). Kernels are assigned to a species according to the super-types its mode of dispersal belongs to.
disp_groups.df %>%
mutate(Details = replace_na(Details, "-")) %>%
knitr::kable(caption = "Reference values of dispersal distances (m) reported in @Vittoz&Engler2007_dispersal are used to verify the values generated by the kernels in the [Dispersal kernels](#dispersal-kernels) section of [Implementation](#implementation)",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
pack_rows(index = c("Short-distance dispersal kernel (0.1-100 m)" = 12,
"Medium-distance dispersal kernel (100-500 m)" = 1,
"Long-distance dispersal kernel (1-10 km)" = 8,
"Special cases: Dispersal through any of the kernels" = 4))| Dispersal mode | Quantiles (50-99% m) | Details |
|---|---|---|
| Short-distance dispersal kernel (0.1-100 m) | ||
| Autochory | 0.1-1 |
|
| Blastochory | 0.1-1 |
|
| Herpochory | NA | not included in Vittoz & Engler (2007) but assigned the same as other autochory syndromes |
| Ballochory | 1-5 |
|
| Boleochory | 0.1-1 |
|
| Ombrochory | 0.1-1 |
|
| Cystometeorochory | 1-5 |
|
| Trichometeorochory | 2-15 |
|
| Meteorochory | NA |
|
| Pterometeorochory | 2-15 |
|
| Chamaeochory | 1-5 |
|
| Myrmechory | 2-15 |
|
| Medium-distance dispersal kernel (100-500 m) | ||
| Dyszoochory | 40-150 |
|
| Long-distance dispersal kernel (1-10 km) | ||
| Agochory | 500-5000 |
|
| Anthropochory | 500-5000 |
|
| Endozoochory | 400-1500 |
|
| Epizoochory | 400-1500 |
|
| Ethelochory | 500-5000 | assigned the same as anthropochory |
| Hemerochory | NA |
|
| Speirochory | 500-5000 | assigned the same as anthropochory |
| Zoochory | 400-1500 |
|
| Special cases: Dispersal through any of the kernels | ||
| Anemochory | NA |
|
| Nautochory | NA |
|
| Bythisochory | NA |
|
| other | NA |
|
When more than one dispersal kernel suits the same species, all of them are listed. If no dispersal mode was reported on neither of the references, the species can spread via any of the kernels. Because of the difficult in estimating the dispersal distances reached by nautochoric and bythisochoric species, those dispersal modes were ignored for species that had other dispersal modes. Species that only dispersed via these two modes were assigned as being able to disperse via any of the dispersal kernels.
## combine information source on dispersal: LEDA and classification of Vittoz & Engler (2007)
dispersal_edm <- spp_list %>%
left_join(., dispersal_leda) %>% # column type
dplyr::select(sp, mode_leda) %>%
left_join(., dispersal_ve) %>% # columns max_dist, disp_mode_ve, genus_max, genus_mode
distinct() %>%
## Reclassify dispersal modes into dispersal kernels, according to Vittoz & Engler (2007).
dplyr::select(sp, mode_leda, disp_mode_ve, genus_mode_ve) %>%
## If the dispersal mode for a species is missing in the LEDA traitbase, it get the mode reported in @vittozseed2007. If missing in both, the species getsthe dispersal mode reported for the same genus in @vittozseed2007 (`dispersal_kernels` table).
mutate(mode_leda = case_when(
(is.na(mode_leda) | mode_leda == "") ~ ifelse(is.na(disp_mode_ve), genus_mode_ve, disp_mode_ve),
TRUE ~ as.character(mode_leda))) %>%
dplyr::select(-genus_mode_ve) %>%
## bythiosochory and nautochory: if it's a duplicate, delete; otherwise, mark as NA
filter(., !(mode_leda %in% c("nautochor", "bythiosochor") & duplicated(sp))) %>%
mutate(mode_leda = case_when(
!(mode_leda %in% c("nautochor", "bythisochor")) ~ as.character(mode_leda))) %>%
mutate(mode_leda = case_when(
!(mode_leda %in% c("nautochor", "bythisochor")) ~ as.character(mode_leda))) %>%
## assign values reported in Vittoz & Engler (2007) to LEDA NAs and vice-versa
mutate(mode_leda = case_when(
is.na(mode_leda) & !is.na(disp_mode_ve) ~ disp_mode_ve,
TRUE ~ mode_leda)) %>%
mutate(disp_mode_ve = case_when(
!is.na(mode_leda) & is.na(disp_mode_ve) ~ mode_leda,
TRUE ~ disp_mode_ve)) %>%
## identify short-, medium-, and long-distance kernels reported in LEDA database
mutate(
mode_leda = case_when(
mode_leda %in% c("autochor", "blastochor", "herpochor", "ballochor", "blastochor", "boleochor", "ombrochor", "cystometeorochor", "trichometeorochor", "meteorochor", "pterometeorochor", "myrmechor", "chamaechor") ~ "short",
mode_leda %in% c("dysochor") ~ "medium",
mode_leda %in% c("agochor", "anthropochor", "epizoochor", "endozoochor", "ethelochor", "hemerochor", "speirochor", "zoochor") ~ "long",
mode_leda %in% c("anemochor", "other") ~ "any",
TRUE ~ mode_leda)) %>%
## identify short-, medium-, and long-distance kernels reported in Vittoz & Engler (2007)
mutate(
disp_mode_ve = case_when(
disp_mode_ve %in% c("autochor", "blastochor", "herpochor", "ballochor", "boleochor", "ombrochor", "cystometeorochor", "trichometeorochor", "meteorochor", "pterometeorochor", "myrmechor", "chamaechor") ~ "short",
disp_mode_ve %in% c("dysochor") ~ "medium",
disp_mode_ve %in% c("agochor", "anthropochor", "endozoochor", "epizoochor", "ethelochor", "hemerochor", "speirochor", "zoochor") ~ "long",
disp_mode_ve %in% c("anemochor", "other") ~ "any",
TRUE ~ disp_mode_ve)) %>%
## merge LEDA and Vittoz & Engler (2007) reports
mutate(kernel = case_when(
mode_leda == "any" | disp_mode_ve == "any" ~ "any",
is.na(mode_leda) & is.na(disp_mode_ve) ~ "any",
mode_leda == disp_mode_ve ~ mode_leda,
mode_leda != disp_mode_ve ~ paste(mode_leda, disp_mode_ve, sep = "-"))) %>%
distinct() %>%
## Merge multiple dispersal types reported for the same species
dplyr::select(sp, kernel) %>%
group_by(sp) %>%
mutate(kernel = paste0(kernel, collapse = "-")) %>%
mutate(kernel = case_when(
## identify species for which any kernel can be used
(str_detect(kernel, "any") | all(str_detect(kernel, c("long", "short", "medium")))) ~ "any",
## remove repeated occurences
str_count(kernel, "short") > 1 ~ str_remove(kernel, "short-"),
str_count(kernel, "medium") > 1 ~ str_remove(kernel, "medium-"),
str_count(kernel, "long") > 1 ~ str_remove(kernel, "long-"),
TRUE ~ as.character(kernel))) %>%
distinct()Each super-type is modeled by a set of kernel parameters Table @ref(tab:kernels).
kernels.df %>%
knitr::kable(caption = "Dispersal kernels simulated in the model and the range of dispersal distances generated by each.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) #%>%| Dispersal kernel | Function (parameters) | Range of values |
|---|---|---|
| Short-distance | Inverse Gaussian \((\mu = 1, \lambda = 0.2) \times 4\) | \(0.1-100 m\) |
| Medium-distance | Inverse Gaussian \((\mu = 0.2, \lambda = 3) \times 1000\) | \(100-500 m\) |
| Long-distance | Inverse Gaussian \((\mu = 1000, \lambda = 100\)) | \(1-10 km\) |
## kable_styling(full_width = FALSE) %>%
## column_spec(1, width = "4cm") %>%
## column_spec(2, width = "10cm") %>%
## column_spec(3, width = "4cm")3.3.2 Seed mass
Seed mass values were retrieved from the LEDA database [@kleyerleda2008] and from a classification of plant sizes by @weissgrazing2014.
## filter values from Goettigen species
seedmass_goet <- seedmass_leda%>%
dplyr::select(sp, diaspore, single_value, mean_sm)%>%
mutate(mean_sm = mean_sm/1000)%>% #convert to milligrams
mutate(single_value = single_value/1000)%>% #convert to milligrams
group_by(sp,diaspore)%>%
left_join(spp_list,.)%>%
distinct()In the LEDA database, mean_sm refers to the mean diaspore(*) mass value calculated from multiple observations, and single_value, to the measure of a single observation [@kleyerleda2008].
For some species, however, "single value" and "mean" values are not the same or one of them is missing. When available, both values are rather close ("Discrepancies are small", in Table @ref(tab:seedmass-values)). Nonetheless, mean_sm values are preferred, as they arise from multiple observations. If mean_sm is missing, single_value is used.
seedmass_goet %>%
mk_itspp() %>%
filter(single_value != mean_sm | is.na(single_value) | is.na(mean_sm)) %>%
mutate(value_group = case_when(
is.na(single_value) & is.na(mean_sm) ~ "Mean and single value are missing",
!(is.na(single_value)) & is.na(mean_sm) ~ "Only mean value is missing",
(is.na(single_value)) & !(is.na(mean_sm)) ~ "Only single value is missing",
TRUE ~ "Discrepancies are small")) %>%
dplyr::select(Species = species, Availability = value_group) %>%
arrange(Availability) %>%
knitr::kable(caption = "Identification of missing values for species affected by it.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
collapse_rows(columns = 2, valign = "top") %>%
footnote(general = "Daucus carota and Rumex acetosa have multiple non-matching entries in LEDA, but mean value is available and will thus will be used.")| Species | Availability |
|---|---|
| Aegopodium podagraria | Discrepancies are small |
| Chaerophyllum temulum | |
| Cichorium intybus | |
| Daucus carota | |
| Geranium molle | |
| Geranium robertianum | |
| Holcus lanatus | |
| Rhinanthus angustifolius | |
| Rumex acetosa | |
| Rumex acetosa | |
| Tanacetum vulgare | |
| Trifolium dubium | |
| Carex leporina | Mean and single value are missing |
| Carlina vulgaris | |
| Cephalanthera rubra | |
| Cerastium holosteoides | |
| Cerinthe minor | |
| Elytrigia repens | |
| Equisetum arvense | |
| Hieracium lachenalii | |
| Inula conyzae | |
| Listera ovata | |
| Medicago falcata | |
| Mentha verticillata | |
| Ophrys apifera | |
| Ophrys insectifera | |
| Orchis mascula | |
| Orchis purpurea | |
| Orchis tridentata | |
| Potentilla neumanniana | |
| Senecio ovatus | |
| Taraxacum laevigatum | |
| Veronica teucrium | |
| Vicia villosa | |
| Allium schoenoprasum | Only mean value is missing |
| Daucus carota | |
| Rumex acetosa | |
| Triticum aestivum | |
| Note: | |
| Daucus carota and Rumex acetosa have multiple non-matching entries in LEDA, but mean value is available and will thus will be used. |
Note that the reported values (of mean_sm and single_value) vary according to the diaspore, dispersule or geminule, used to measure it:
"Dispersule: Every morphological part of a plant that serves as a unit of dispersal and becomes detached from the mother-plant to disperse. Here we only provide data for the generative dispersules, i.e. units of dispersal that contain a seeds (see also germinule)."
"Germinule: Unit of germination. In many cases the dispersule is not the unit that will enter the soil after dispersal and germinate and therefore differs from the dispersule. This difference is due to morphological structures, such as pappus, wings, awns or fleshy nutrient containing tissues, that get lost between the time of dispersal and the time of germination."
--- Definitions used in the LEDA traitbase [@kleyerleda2008].
Discrepant measures could be cleared identified for the one instance of "multi-seeded generative" diaspore, which was thus discarded. Values reported for "unknown" diaspore were not used either, as it is not possible to evaluate for which reproductive unit they were measured. The other variances were checked via their coefficient of variation. Values of germinule weight have precedence, as these are the reproductive structures that enter the soil and germinate. Species for which germinule value was missing were assigned the value reported for one-seeded dispersule. If that was also missing, the value reported for generative dispersule was used.
seedmass_germinule <- seedmass_goet %>%
filter(diaspore == "germinule")%>%
mutate(mean_sm = case_when(
is.na(mean_sm) ~ single_value,
TRUE ~ mean_sm))%>%
dplyr::select(-single_value)%>%
distinct()%>%
group_by(sp,diaspore)%>%
summarize(seedmass_mean = mean(mean_sm),
seedmass_sd = sd(mean_sm),
seedmass_cv = 100*seedmass_sd/seedmass_mean)
seedmass_germinule %>%
mk_itspp() %>%
dplyr::select(-diaspore) %>%
rename("Species" = species,
"Mean value (mg)" = seedmass_mean,
"Std. Deviation (mg)" = seedmass_sd,
"Coeff. variation (%)" = seedmass_cv) %>%
kable(caption = "Intraspecific variation of germinule mass values reported in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "400px")| Species | Mean value (mg) | Std. Deviation (mg) | Coeff. variation (%) |
|---|---|---|---|
| Achillea millefolium | 0.0001305 | 0.0000423 | 32.402769 |
| Acinos arvensis | 0.0006155 | 0.0006515 | 105.842074 |
| Actaea spicata | 0.0062603 | 0.0006095 | 9.736148 |
| Aegopodium podagraria | 0.0015825 | 0.0004878 | 30.824561 |
| Agrimonia eupatoria | 0.0130967 | 0.0104114 | 79.496415 |
| Ajuga genevensis | 0.0017540 | NA | NA |
| Ajuga reptans | 0.0014652 | 0.0000206 | 1.408543 |
| Allium schoenoprasum | 0.0009167 | 0.0001735 | 18.925650 |
| Alopecurus pratensis | 0.0007005 | 0.0000742 | 10.599031 |
| Anchusa arvensis | 0.0045297 | 0.0015999 | 35.320013 |
| Antennaria dioica | 0.0000535 | 0.0000092 | 17.182034 |
| Anthoxanthum odoratum | 0.0005640 | 0.0002985 | 52.931100 |
| Anthriscus sylvestris | 0.0042036 | 0.0008762 | 20.844473 |
| Anthyllis vulneraria | 0.0032066 | 0.0005561 | 17.342779 |
| Arabis hirsuta | 0.0000926 | 0.0000336 | 36.256428 |
| Arrhenatherum elatius | 0.0025858 | 0.0005373 | 20.779781 |
| Artemisia vulgaris | 0.0001396 | 0.0000387 | 27.737779 |
| Astragalus glycyphyllos | 0.0051500 | 0.0004784 | 9.289591 |
| Avenula pubescens | 0.0019075 | 0.0000233 | 1.223304 |
| Bellis perennis | 0.0001415 | 0.0000809 | 57.199302 |
| Brachypodium pinnatum | 0.0020458 | 0.0012759 | 62.366970 |
| Briza media | 0.0003855 | 0.0001598 | 41.451004 |
| Bromus erectus | 0.0046908 | 0.0003932 | 8.382814 |
| Bromus hordeaceus | 0.0017159 | 0.0018782 | 109.459352 |
| Calystegia sepium | 0.0302720 | 0.0042330 | 13.983189 |
| Campanula glomerata | 0.0001430 | 0.0000268 | 18.720721 |
| Campanula persicifolia | 0.0000706 | 0.0000079 | 11.217558 |
| Campanula rapunculoides | 0.0001920 | 0.0001645 | 85.695211 |
| Campanula rotundifolia | 0.0000631 | 0.0000117 | 18.565796 |
| Cardaminopsis arenosa | 0.0001090 | NA | NA |
| Carduus crispus | 0.0016901 | 0.0005185 | 30.678209 |
| Carex caryophyllea | 0.0012118 | 0.0003657 | 30.177066 |
| Carex flacca | 0.0009653 | 0.0001954 | 20.245877 |
| Carex ornithopoda | 0.0007596 | 0.0001126 | 14.819826 |
| Centaurea jacea | 0.0020156 | 0.0009595 | 47.603449 |
| Centaurea scabiosa | 0.0061623 | 0.0011812 | 19.168865 |
| Centaurium erythraea | 0.0000126 | 0.0000043 | 33.937470 |
| Cerastium arvense | 0.0002260 | NA | NA |
| Cerastium tomentosum | 0.0004492 | 0.0001000 | 22.267996 |
| Chaerophyllum hirsutum | 0.0033330 | NA | NA |
| Chaerophyllum temulum | 0.0028661 | 0.0004814 | 16.794404 |
| Cichorium intybus | 0.0019306 | 0.0003673 | 19.028123 |
| Cirsium acaule | 0.0033513 | 0.0007729 | 23.063137 |
| Cirsium arvense | 0.0011891 | 0.0001025 | 8.621032 |
| Cirsium oleraceum | 0.0025923 | 0.0000881 | 3.399079 |
| Cirsium vulgare | 0.0024458 | 0.0004116 | 16.827812 |
| Clinopodium vulgare | 0.0004245 | 0.0000575 | 13.539201 |
| Convolvulus arvensis | 0.0114391 | 0.0125680 | 109.868796 |
| Crepis biennis | 0.0011153 | 0.0001645 | 14.746977 |
| Cruciata laevipes | 0.0035856 | NA | NA |
| Cynosurus cristatus | 0.0005514 | 0.0001141 | 20.697872 |
| Dactylis glomerata | 0.0009057 | 0.0002729 | 30.131669 |
| Daucus carota | 0.0011330 | 0.0003149 | 27.791489 |
| Echium vulgare | 0.0028145 | 0.0002831 | 10.058813 |
| Epilobium angustifolium | 0.0000564 | NA | NA |
| Epilobium montanum | 0.0001204 | NA | NA |
| Euphorbia cyparissias | 0.0019580 | 0.0007750 | 39.580645 |
| Euphrasia officinalis | 0.0001310 | 0.0000014 | 1.079552 |
| Falcaria vulgaris | 0.0009300 | NA | NA |
| Festuca ovina | 0.0003565 | 0.0002150 | 60.300009 |
| Festuca pratensis | 0.0018080 | 0.0003932 | 21.745098 |
| Festuca rubra | 0.0008650 | 0.0003040 | 35.138880 |
| Filipendula vulgaris | 0.0007030 | 0.0001601 | 22.778171 |
| Fragaria vesca | 0.0003208 | 0.0000266 | 8.292306 |
| Fragaria viridis | 0.0003751 | 0.0001398 | 37.275672 |
| Galeopsis ladanum | 0.0012000 | NA | NA |
| Galium aparine | 0.0092078 | 0.0025501 | 27.694628 |
| Galium mollugo | 0.0007562 | 0.0001813 | 23.980360 |
| Galium verum | 0.0005547 | 0.0001168 | 21.053955 |
| Genista tinctoria | 0.0037611 | 0.0005859 | 15.576876 |
| Gentianella ciliata | 0.0001270 | NA | NA |
| Gentianella germanica | 0.0001544 | 0.0000288 | 18.685205 |
| Geranium dissectum | 0.0025653 | 0.0000888 | 3.462393 |
| Geranium molle | 0.0010464 | 0.0000799 | 7.636833 |
| Geranium pratense | 0.0083606 | 0.0018614 | 22.263458 |
| Geranium pusillum | 0.0007572 | 0.0001216 | 16.065116 |
| Geranium robertianum | 0.0014076 | 0.0002583 | 18.349480 |
| Geum urbanum | 0.0015905 | 0.0012141 | 76.334633 |
| Gymnadenia conopsea | 0.0000060 | NA | NA |
| Helianthemum nummularium | 0.0010792 | 0.0003380 | 31.317262 |
| Heracleum sphondylium | 0.0059122 | 0.0006622 | 11.200961 |
| Hieracium murorum | 0.0004000 | NA | NA |
| Hieracium pilosella | 0.0001724 | 0.0000573 | 33.226069 |
| Hippocrepis comosa | 0.0036646 | 0.0005812 | 15.858910 |
| Holcus lanatus | 0.0003542 | 0.0001032 | 29.145509 |
| Hypericum perforatum | 0.0000995 | 0.0000354 | 35.527311 |
| Knautia arvensis | 0.0044278 | 0.0017531 | 39.592036 |
| Koeleria pyramidata | 0.0023000 | NA | NA |
| Lathyrus pratensis | 0.0126003 | 0.0022036 | 17.488367 |
| Leontodon hispidus | 0.0011093 | 0.0002842 | 25.620397 |
| Leucanthemum vulgare | 0.0004056 | 0.0000496 | 12.227313 |
| Linum catharticum | 0.0001529 | 0.0000158 | 10.303890 |
| Lolium perenne | 0.0019982 | 0.0002313 | 11.574323 |
| Lotus corniculatus | 0.0013993 | 0.0002433 | 17.387347 |
| Medicago lupulina | 0.0017413 | 0.0002380 | 13.668272 |
| Melilotus alba | 0.0028295 | 0.0012594 | 44.508117 |
| Melilotus officinalis | 0.0025756 | 0.0014815 | 57.519192 |
| Mentha arvensis | 0.0001837 | 0.0000666 | 36.239406 |
| Myosotis arvensis | 0.0003060 | 0.0000224 | 7.320261 |
| Myosotis sylvatica | 0.0004670 | 0.0001315 | 28.163139 |
| Onobrychis viciifolia | 0.0201170 | 0.0023906 | 11.883511 |
| Ononis repens | 0.0051355 | 0.0004122 | 8.027325 |
| Ononis spinosa | 0.0047589 | 0.0015598 | 32.776793 |
| Orchis militaris | 0.0000010 | NA | NA |
| Origanum vulgare | 0.0000944 | 0.0000147 | 15.526569 |
| Phalaris arundinacea | 0.0007175 | 0.0000629 | 8.770896 |
| Pimpinella saxifraga | 0.0012331 | 0.0002822 | 22.889261 |
| Plantago lanceolata | 0.0017259 | 0.0004097 | 23.740868 |
| Plantago major | 0.0002950 | 0.0000636 | 21.572749 |
| Plantago media | 0.0003732 | 0.0000919 | 24.608892 |
| Platanthera chlorantha | 0.0000030 | NA | NA |
| Poa annua | 0.0002127 | 0.0000610 | 28.702477 |
| Poa pratensis | 0.0002687 | 0.0000318 | 11.830921 |
| Polygala vulgaris | 0.0017736 | 0.0001246 | 7.026344 |
| Potentilla anserina | 0.0010118 | 0.0001875 | 18.533773 |
| Potentilla reptans | 0.0002679 | 0.0000220 | 8.228751 |
| Primula veris | 0.0008156 | 0.0001858 | 22.783230 |
| Prunella grandiflora | 0.0007780 | NA | NA |
| Prunella vulgaris | 0.0006776 | 0.0000659 | 9.719330 |
| Ranunculus bulbosus | 0.0026607 | 0.0011067 | 41.596272 |
| Ranunculus repens | 0.0022736 | 0.0007780 | 34.218060 |
| Rhinanthus angustifolius | 0.0023213 | 0.0002768 | 11.923573 |
| Rhinanthus minor | 0.0027015 | 0.0004292 | 15.886784 |
| Rumex acetosa | 0.0008381 | 0.0002866 | 34.200062 |
| Rumex crispus | 0.0018993 | 0.0008908 | 46.898441 |
| Salvia pratensis | 0.0026018 | 0.0005634 | 21.653102 |
| Sanguisorba minor | 0.0043664 | 0.0015513 | 35.528058 |
| Scabiosa columbaria | 0.0017925 | 0.0005773 | 32.209043 |
| Sedum sexangulare | 0.0000100 | NA | NA |
| Senecio erucifolius | 0.0003100 | NA | NA |
| Senecio jacobaea | 0.0003171 | 0.0001260 | 39.747534 |
| Senecio vulgaris | 0.0002690 | 0.0000198 | 7.360219 |
| Silene dioica | 0.0007876 | 0.0001729 | 21.955568 |
| Silene nutans | 0.0003454 | 0.0001316 | 38.109924 |
| Sonchus asper | 0.0002943 | 0.0000257 | 8.730561 |
| Stachys germanica | 0.0015188 | 0.0002237 | 14.729191 |
| Stellaria holostea | 0.0027284 | 0.0004172 | 15.292532 |
| Stellaria media | 0.0003936 | 0.0000686 | 17.427629 |
| Symphytum officinale | 0.0111288 | NA | NA |
| Tanacetum vulgare | 0.0003431 | 0.0001686 | 49.131722 |
| Thymus pulegioides | 0.0001519 | 0.0000300 | 19.724965 |
| Tragopogon pratensis | 0.0084214 | 0.0013562 | 16.103975 |
| Trifolium campestre | 0.0003186 | 0.0000699 | 21.952155 |
| Trifolium dubium | 0.0004029 | 0.0000441 | 10.939470 |
| Trifolium medium | 0.0021266 | 0.0004149 | 19.511439 |
| Trifolium ochroleucon | 0.0023984 | 0.0010093 | 42.082031 |
| Trifolium pratense | 0.0013825 | 0.0002395 | 17.326319 |
| Trifolium repens | 0.0005134 | 0.0001535 | 29.904679 |
| Trisetum flavescens | 0.0002230 | 0.0000753 | 33.787385 |
| Triticum aestivum | 0.0431250 | 0.0008839 | 2.049585 |
| Valeriana dioica | 0.0008636 | 0.0006056 | 70.126026 |
| Valeriana officinalis | 0.0008710 | 0.0002221 | 25.494409 |
| Verbascum lychnitis | 0.0001188 | 0.0000181 | 15.237318 |
| Veronica chamaedrys | 0.0001960 | 0.0000177 | 9.026432 |
| Vicia cracca | 0.0158318 | 0.0044573 | 28.154330 |
| Vicia hirsuta | 0.0059904 | 0.0023536 | 39.290087 |
| Vicia sepium | 0.0227702 | 0.0023650 | 10.386586 |
| Vicia tenuifolia | 0.0242800 | 0.0053457 | 22.016999 |
| Vicia tetrasperma | 0.0031977 | 0.0004474 | 13.991228 |
| Vincetoxicum hirundinaria | 0.0069850 | 0.0003323 | 4.757912 |
| Viola hirta | 0.0028570 | 0.0007985 | 27.950250 |
| Viola odorata | 0.0033096 | NA | NA |
ggplot(seedmass_germinule) +
geom_histogram(aes(x = seedmass_cv)) +
labs(x = "Coefficient of variation (%)")## Warning: Removed 20 rows containing non-finite values (stat_bin).
Distribution of coefficient of intraspecific variation of measures of germinule mass reported in the LEDA database.
seedmass_oneseeded <- seedmass_goet %>%
# these values will be used for species missing the germinule measure: 4 spp
filter(diaspore == "one-seeded_generative_dispersule" &
!(sp %in% seedmass_germinule$sp))%>%
mutate(mean_sm = case_when(
is.na(mean_sm) ~ single_value,
TRUE ~ mean_sm))%>%
dplyr::select(-single_value)%>%
distinct()%>%
group_by(sp,diaspore)%>%
summarize(seedmass_mean = mean(mean_sm),
seedmass_sd = sd(mean_sm),
seedmass_cv = 100*seedmass_sd/seedmass_mean)
seedmass_oneseeded %>%
mk_itspp() %>%
dplyr::select(-diaspore) %>%
rename("Species" = species,
"Mean value (mg)" = seedmass_mean,
"Std. Deviation (mg)" = seedmass_sd,
"Coeff. variation (%)" = seedmass_cv) %>%
kable(caption = "Intra-specific variation of mass values of one-seed generative dispersules reported in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Species | Mean value (mg) | Std. Deviation (mg) | Coeff. variation (%) |
|---|---|---|---|
| Hieracium caespitosum | 0.0001065 | NA | NA |
| Melampyrum sylvaticum | 0.0075550 | NA | NA |
| Phleum pratense | 0.0004460 | NA | NA |
| Poa trivialis | 0.0001440 | 0.0000581 | 40.34955 |
seedmass_gendisp <- seedmass_goet %>%
# these values will be used for species missing the germinule measure: 3 spp
filter(diaspore == "generative_dispersule" &
!(sp %in% c(seedmass_germinule$sp, seedmass_oneseeded$sp)))%>%
mutate(mean_sm = case_when(
is.na(mean_sm) ~ single_value,
TRUE ~ mean_sm))%>%
dplyr::select(-single_value)%>%
distinct()%>%
group_by(sp,diaspore)%>%
summarize(seedmass_mean = mean(mean_sm, na.rm = TRUE),
seedmass_sd = sd(mean_sm, na.rm = TRUE),
seedmass_cv = 100*seedmass_sd/seedmass_mean)
seedmass_gendisp %>%
dplyr::select(-diaspore) %>%
mk_itspp() %>%
rename("Species" = species,
"Mean value (mg)" = seedmass_mean,
"Std. Deviation (mg)" = seedmass_sd,
"Coeff. variation (%)" = seedmass_cv) %>%
kable(caption = "Intra-specific variation of mass values of generative dispersules reported in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Species | Mean value (mg) | Std. Deviation (mg) | Coeff. variation (%) |
|---|---|---|---|
| Galium pumilum | 0.0003580 | NA | NA |
| Melampyrum nemorosum | 0.0066378 | NA | NA |
| Polygala comosa | 0.0014200 | NA | NA |
For species for which data is missing in the LEDA database, we used the values attributed in the @weissgrazing2014 classification of species sizes.
seedmass_weiss <- filter(weiss_goetmasses, !(sp %in% c(seedmass_germinule$sp,
seedmass_oneseeded$sp,
seedmass_gendisp$sp)))
seedmass_weiss %>%
mk_itspp() %>%
dplyr::select("Species" = species,
"Seed mass (mg)" = weiss_seedmass)%>%
kable(caption = "Species for which seed mass could not be retrieved from the LEDA database",longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Species | Seed mass (mg) |
|---|---|
| Equisetum arvense | 0.0001 |
| Veronica teucrium | 0.0003 |
The variation of reported values (Table @ref(tab:germinule-leda), Fig. @ref(fig:fig-germ-leda), and Table @ref(tab:oneseed-leda)) is passed down to the model by inputing minimal and maximal values, from which individual values are drawn, following an Uniform distribution.
# Combine all measures in a single table and reclassify LEDA values inside Weiss et al.
seedmass_edm <- full_join(seedmass_germinule, seedmass_oneseeded)%>%
full_join(., seedmass_gendisp)%>%
full_join(., seedmass_weiss)%>%
mutate(seedmass_mean = case_when(
is.na(seedmass_mean) ~ weiss_seedmass,
TRUE ~ seedmass_mean))%>%
mutate(seedmass_mean = case_when(
seedmass_mean <= 0.0001 ~ 0.0001,
seedmass_mean > 0.0001 & seedmass_mean <= 0.0003 ~ 0.0003,
seedmass_mean > 0.0003 ~ 0.001))%>%
dplyr::select(sp, seedmass = seedmass_mean)%>%
full_join(., spp_list)Seed mass sizes were used to parameterize metabolic rates of germination (see below in Implementation verification).
3.3.3 Adult maximal mass (Table @ref(tab:size-classes))
We combined the allometric relationship devised by @niklasenquistcanonical2002 with the classification divised by @weissgrazing2014 to assign species maximal (adult) weights.
Considering the biomass allometric relationship, \(M_{leaf} \U+221D M_{stem}^{3/4} \U+221D M_{root}^{3/4}\), the total maximal biomass of adult individuals is \(M_{total} = 2 \times M_{organ} + M_{organ}^{3/4}\). We used @weissgrazing2014 size group classification Table @ref(tab:size-classes) to assign organ maximal biomass (also referred to as biomass compartments) according to the species seed size. Forty-seven of the 194 species used to parameterize the model were used to devise this classification.
fullsize <- function(x){
fullsize = 2*x + x^(3/4)
return(fullsize)
}
maxmass_edm <- seedmass_edm%>%
mutate(maxmass = case_when(
seedmass <= 0.0001 ~ fullsize(10),
seedmass > 0.0001 & seedmass <= 0.0003 ~ fullsize(20),
seedmass > 0.0003 ~ fullsize(50)))%>%
dplyr::select(sp, maxmass)| Group | Seed mass (mg) | Max. size (g) |
|---|---|---|
| small | 0.0001 | 1 |
| medium | 0.0003 | 2 |
| large | 0.0010 | 5 |
3.3.4 Seed number
Seed numbers were retrieved from the LEDA database [@kleyerleda2008]. Similar to values of seed mass, measures from multiple (mean_value) and a single (single_value) observations, and for different types of inflorescence were reported.
Because both measures are rather close @ref(fig:match-seednumbers), and there are less species for which single_value is missing @ref(tab:nas-seednumbers), this value was used for all species.
## filter values for species from Göttingen
seednumber_goet <- seednumber_leda %>%
left_join(spp_list,.)
seednumber_goet %>%
mutate(ratio = mean_number/single_value) %>%
ggplot(aes(x = ratio)) +
geom_histogram(bin_width = 100)+
labs(x = "Ratio mean_number/single value")## Warning: Ignoring unknown parameters: bin_width## Warning: Removed 353 rows containing non-finite values (stat_bin).
Comparison of values reported as the "mean" and "single" seed numbers in the LEDA trait database.
tbdf <- seednumber_goet %>%
dplyr::select(mean_number, single_value) %>%
mutate_at(.vars = vars(mean_number, single_value), .funs = is.na)
do.call(cbind, lapply(tbdf, summary)) %>%
kable(caption = "Summary of missing data on seed number of the species used to paramterize the model, in the LEDA database. \"TRUE\" and \"FALSE\" refer to the availability of data or not, respectively.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| mean_number | single_value | |
|---|---|---|
| Mode | logical | logical |
| FALSE | 583 | 872 |
| TRUE | 353 | 64 |
The value reported varies according to the reproductive unit it was measured on. The possible reproductive units are "multiple flower stem", "ramet/tussock or individual plant", "single flower inflorescence", "multiple flower inflorescence", "per square meter", or "unknown" (Table @ref(tab:seednumber-units)).
# Average multiple measures for the same reproductive unit
seed_number_unit <- seednumber_goet%>%
group_by(sp,repro_unit)%>%
summarize(seednumber_mean = mean(single_value),
seednumber_sd = sd(single_value),
seednumber_min = min(single_value),
seednumber_max = max(single_value))%>%
filter(repro_unit != "per_square_meter") #Filter out NAs and measures per area
seed_number_unit %>%
mk_itspp() %>%
mutate(repro_unit = str_replace_all(repro_unit, pattern = "_", replacement = " ")) %>%
rename("Species" = "species",
"Reproductive unit" = "repro_unit",
"Seed number (\U1D707) " = "seednumber_mean",
"Seed number (std. dev.)" = "seednumber_sd",
"Seed number (min.)" = "seednumber_min",
"Seed number (max.)" = "seednumber_max") %>%
kable(caption = "Summary of seed number values reported in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover",
"condensed", "responsive")) %>%
scroll_box(width = "100%", height = "400px")| Species | Reproductive unit | Seed number (𝜇) | Seed number (std. dev.) | Seed number (min.) | Seed number (max.) |
|---|---|---|---|---|---|
| Achillea millefolium | per multiple flower stem | 822.00000 | NA | 822.00 | 822.00 |
| Achillea millefolium | per ramet/tussock or individual plant | 2473.01667 | 1923.911755 | 190.00 | 7200.00 |
| Acinos arvensis | per ramet/tussock or individual plant | 342.00000 | NA | 342.00 | 342.00 |
| Actaea spicata | per ramet/tussock or individual plant | 574.25000 | 884.159252 | 108.00 | 1900.00 |
| Actaea spicata | per single flower inflorescence | 12.00000 | NA | 12.00 | 12.00 |
| Aegopodium podagraria | per multiple flower inflorescence | 317.00000 | 68.687699 | 240.00 | 402.00 |
| Aegopodium podagraria | per multiple flower stem | 199.90000 | 29.357755 | 164.00 | 253.00 |
| Aegopodium podagraria | per ramet/tussock or individual plant | 2075.16250 | 3589.605562 | 46.50 | 10700.00 |
| Agrimonia eupatoria | per multiple flower stem | 50.00000 | NA | 50.00 | 50.00 |
| Agrimonia eupatoria | per ramet/tussock or individual plant | 66.00000 | NA | 66.00 | 66.00 |
| Agrimonia eupatoria | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Ajuga genevensis | per multiple flower stem | 221.00000 | NA | 221.00 | 221.00 |
| Ajuga reptans | per multiple flower inflorescence | 21.00000 | NA | 21.00 | 21.00 |
| Ajuga reptans | per ramet/tussock or individual plant | 56.75000 | 32.592177 | 36.00 | 105.00 |
| Allium schoenoprasum | per multiple flower stem | 162.00000 | NA | 162.00 | 162.00 |
| Allium schoenoprasum | per ramet/tussock or individual plant | 4149.00000 | NA | 4149.00 | 4149.00 |
| Alopecurus pratensis | per ramet/tussock or individual plant | 414.00000 | NA | 414.00 | 414.00 |
| Anchusa arvensis | per ramet/tussock or individual plant | 557.00000 | 286.000000 | 128.00 | 700.00 |
| Anchusa arvensis | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Antennaria dioica | per ramet/tussock or individual plant | 280.00000 | NA | 280.00 | 280.00 |
| Anthoxanthum odoratum | per multiple flower inflorescence | 14.55455 | 2.678195 | 9.00 | 19.60 |
| Anthoxanthum odoratum | per ramet/tussock or individual plant | 401.87500 | 432.510958 | 108.00 | 1038.50 |
| Anthriscus sylvestris | per multiple flower inflorescence | 229.50000 | 113.844192 | 149.00 | 310.00 |
| Anthriscus sylvestris | per multiple flower stem | 2457.00000 | NA | 2457.00 | 2457.00 |
| Anthriscus sylvestris | per ramet/tussock or individual plant | 3053.56875 | 3723.619305 | 132.10 | 10000.00 |
| Anthriscus sylvestris | unknown | 5400.00000 | NA | 5400.00 | 5400.00 |
| Anthyllis vulneraria | per multiple flower inflorescence | 16.60000 | NA | 16.60 | 16.60 |
| Anthyllis vulneraria | per ramet/tussock or individual plant | 112.00000 | 115.965512 | 30.00 | 194.00 |
| Arabis hirsuta | per ramet/tussock or individual plant | 750.00000 | NA | 750.00 | 750.00 |
| Arrhenatherum elatius | per ramet/tussock or individual plant | 10.00000 | NA | 10.00 | 10.00 |
| Artemisia vulgaris | per multiple flower inflorescence | 200000.00000 | NA | 200000.00 | 200000.00 |
| Artemisia vulgaris | per ramet/tussock or individual plant | 358333.33333 | 150692.844333 | 200000.00 | 500000.00 |
| Astragalus glycyphyllos | per ramet/tussock or individual plant | 10315.00000 | NA | 10315.00 | 10315.00 |
| Avenula pubescens | per multiple flower inflorescence | 47.90000 | 7.465923 | 43.50 | 59.00 |
| Avenula pubescens | per ramet/tussock or individual plant | 4000.00000 | 0.000000 | 4000.00 | 4000.00 |
| Bellis perennis | per multiple flower inflorescence | 200.00000 | NA | 200.00 | 200.00 |
| Bellis perennis | per multiple flower stem | 125.00000 | NA | 125.00 | 125.00 |
| Bellis perennis | per ramet/tussock or individual plant | 1288.00000 | NA | 1288.00 | 1288.00 |
| Brachypodium pinnatum | per multiple flower stem | 87.00000 | NA | 87.00 | 87.00 |
| Briza media | per ramet/tussock or individual plant | 3743.50000 | 5040.964243 | 179.00 | 7308.00 |
| Falcaria vulgaris | per ramet/tussock or individual plant | 2000.00000 | NA | 2000.00 | 2000.00 |
| Festuca ovina | per multiple flower inflorescence | 38.20000 | 23.401816 | 1.00 | 75.00 |
| Festuca ovina | per multiple flower stem | 62.00000 | NA | 62.00 | 62.00 |
| Festuca ovina | per ramet/tussock or individual plant | 847.00000 | NA | 847.00 | 847.00 |
| Festuca rubra | per multiple flower inflorescence | 7.20000 | NA | 7.20 | 7.20 |
| Festuca rubra | per ramet/tussock or individual plant | 465.00000 | NA | 465.00 | 465.00 |
| Filipendula vulgaris | per ramet/tussock or individual plant | 324.43333 | 183.335112 | 113.00 | 439.30 |
| Fragaria vesca | per multiple flower stem | 174.43333 | 64.882227 | 115.40 | 243.90 |
| Fragaria vesca | per ramet/tussock or individual plant | 290.50000 | 53.294778 | 225.00 | 341.00 |
| Galeopsis ladanum | per ramet/tussock or individual plant | 300.00000 | NA | 300.00 | 300.00 |
| Galium aparine | per ramet/tussock or individual plant | 441.63636 | 307.131982 | 31.00 | 1000.00 |
| Galium aparine | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Galium mollugo | per ramet/tussock or individual plant | 10867.00000 | 12916.012465 | 1734.00 | 20000.00 |
| Galium verum | per multiple flower inflorescence | 550.00000 | NA | 550.00 | 550.00 |
| Galium verum | per ramet/tussock or individual plant | 58.35000 | 64.700271 | 12.60 | 104.10 |
| Galium verum | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Gentianella ciliata | per single flower inflorescence | 670.00000 | NA | 670.00 | 670.00 |
| Gentianella germanica | per ramet/tussock or individual plant | 347.00000 | 187.945027 | 160.00 | 594.00 |
| Gentianella germanica | per single flower inflorescence | 99.00000 | NA | 99.00 | 99.00 |
| Geranium dissectum | per ramet/tussock or individual plant | 336.25000 | 443.196251 | 95.00 | 1000.00 |
| Geranium dissectum | per single flower inflorescence | 5.00000 | NA | 5.00 | 5.00 |
| Geranium molle | per multiple flower inflorescence | 16.00000 | NA | 16.00 | 16.00 |
| Geranium molle | per ramet/tussock or individual plant | 90.50000 | 13.435029 | 81.00 | 100.00 |
| Geranium molle | per single flower inflorescence | 4.00000 | 1.414214 | 3.00 | 5.00 |
| Geranium pratense | per single flower inflorescence | 3.00000 | NA | 3.00 | 3.00 |
| Geranium pusillum | per ramet/tussock or individual plant | 525.00000 | 318.198052 | 300.00 | 750.00 |
| Geranium robertianum | per ramet/tussock or individual plant | 132.72500 | 119.263165 | 30.00 | 300.00 |
| Geranium robertianum | per single flower inflorescence | 5.00000 | NA | 5.00 | 5.00 |
| Geum urbanum | per multiple flower stem | 558.00000 | NA | 558.00 | 558.00 |
| Geum urbanum | per ramet/tussock or individual plant | 248.25000 | 107.833784 | 172.00 | 324.50 |
| Gymnadenia conopsea | per ramet/tussock or individual plant | 31699.00000 | NA | 31699.00 | 31699.00 |
| Gymnadenia conopsea | per single flower inflorescence | 2480.00000 | 2093.036072 | 1000.00 | 3960.00 |
| Helianthemum nummularium | per ramet/tussock or individual plant | 243.00000 | NA | 243.00 | 243.00 |
| Helianthemum nummularium | per single flower inflorescence | 17.50000 | 3.535534 | 15.00 | 20.00 |
| Heracleum sphondylium | per ramet/tussock or individual plant | 5216.00000 | 3508.378187 | 850.00 | 10005.00 |
| Heracleum sphondylium | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Hieracium caespitosum | per ramet/tussock or individual plant | 612.00000 | NA | 612.00 | 612.00 |
| Hieracium pilosella | per multiple flower inflorescence | 71.75000 | 22.980970 | 55.50 | 88.00 |
| Hieracium pilosella | per ramet/tussock or individual plant | 48.26667 | 21.827933 | 21.00 | 75.00 |
| Hippocrepis comosa | per ramet/tussock or individual plant | 120.00000 | NA | 120.00 | 120.00 |
| Hippocrepis comosa | per single flower inflorescence | 6.00000 | NA | 6.00 | 6.00 |
| Holcus lanatus | per multiple flower inflorescence | 112.20000 | NA | 112.20 | 112.20 |
| Holcus lanatus | per ramet/tussock or individual plant | 177000.00000 | NA | 177000.00 | 177000.00 |
| Hypericum perforatum | per ramet/tussock or individual plant | 20933.60000 | 12189.762869 | 3036.00 | 33000.00 |
| Hypericum perforatum | per single flower inflorescence | 211.33333 | 210.571445 | 56.00 | 451.00 |
| Knautia arvensis | per multiple flower inflorescence | 58.70000 | NA | 58.70 | 58.70 |
| Knautia arvensis | per ramet/tussock or individual plant | 826.84000 | 914.915441 | 66.00 | 2000.00 |
| Koeleria pyramidata | per multiple flower stem | 209.50000 | NA | 209.50 | 209.50 |
| Lathyrus pratensis | per multiple flower inflorescence | 12.26667 | 5.515675 | 4.80 | 18.80 |
| Lathyrus pratensis | per ramet/tussock or individual plant | 124.00000 | 120.144358 | 38.00 | 300.00 |
| Lathyrus pratensis | per single flower inflorescence | 6.00000 | NA | 6.00 | 6.00 |
| Leontodon hispidus | per multiple flower inflorescence | 67.70000 | NA | 67.70 | 67.70 |
| Leontodon hispidus | per ramet/tussock or individual plant | 68.50000 | 3.535534 | 66.00 | 71.00 |
| Leucanthemum vulgare | per multiple flower inflorescence | 185.00000 | 148.492424 | 80.00 | 290.00 |
| Leucanthemum vulgare | per ramet/tussock or individual plant | 3573.79000 | 7961.859141 | 66.10 | 26000.00 |
| Linum catharticum | per ramet/tussock or individual plant | 81.35455 | 58.904607 | 11.40 | 183.00 |
| Linum catharticum | per single flower inflorescence | 10.00000 | NA | 10.00 | 10.00 |
| Listera ovata | per ramet/tussock or individual plant | 85500.00000 | 91216.774773 | 21000.00 | 150000.00 |
| Lotus corniculatus | per ramet/tussock or individual plant | 6328.66667 | 10108.754638 | 345.00 | 18000.00 |
| Medicago lupulina | per multiple flower inflorescence | 15.70000 | NA | 15.70 | 15.70 |
| Medicago lupulina | per ramet/tussock or individual plant | 2103.12500 | 2053.357768 | 100.00 | 6600.00 |
| Medicago lupulina | per single flower inflorescence | 1.00000 | NA | 1.00 | 1.00 |
| Melampyrum arvense | per ramet/tussock or individual plant | 24.66667 | 15.011107 | 10.00 | 40.00 |
| Melampyrum arvense | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Melampyrum nemorosum | per ramet/tussock or individual plant | 225.00000 | 301.227489 | 12.00 | 438.00 |
| Melampyrum sylvaticum | per ramet/tussock or individual plant | 23.77143 | 23.507922 | 9.40 | 76.00 |
| Melilotus alba | per ramet/tussock or individual plant | 160880.62500 | 177778.622524 | 4287.50 | 350000.00 |
| Melilotus alba | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Melilotus officinalis | per ramet/tussock or individual plant | 50050.00000 | 70639.967440 | 100.00 | 100000.00 |
| Melilotus officinalis | per single flower inflorescence | 2.00000 | 1.414214 | 1.00 | 3.00 |
| Mentha arvensis | per ramet/tussock or individual plant | 129.87500 | 87.388381 | 19.50 | 200.00 |
| Myosotis arvensis | per ramet/tussock or individual plant | 900.00000 | 391.578004 | 500.00 | 1400.00 |
| Myosotis arvensis | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Myosotis sylvatica | per ramet/tussock or individual plant | 192.00000 | NA | 192.00 | 192.00 |
| Onobrychis viciifolia | per multiple flower inflorescence | 16.00000 | NA | 16.00 | 16.00 |
| Onobrychis viciifolia | per single flower inflorescence | 1.00000 | NA | 1.00 | 1.00 |
| Ononis repens | per single flower inflorescence | 2.50000 | 2.121320 | 1.00 | 4.00 |
| Ononis spinosa | per ramet/tussock or individual plant | 300.00000 | NA | 300.00 | 300.00 |
| Ononis spinosa | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Ophrys apifera | per ramet/tussock or individual plant | 40000.00000 | NA | 40000.00 | 40000.00 |
| Ophrys apifera | per single flower inflorescence | 1000.00000 | NA | 1000.00 | 1000.00 |
| Orchis purpurea | per single flower inflorescence | 6000.00000 | NA | 6000.00 | 6000.00 |
| Origanum vulgare | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Phalaris arundinacea | per ramet/tussock or individual plant | 3000.00000 | NA | 3000.00 | 3000.00 |
| Phleum pratense | per multiple flower inflorescence | 677.00000 | NA | 677.00 | 677.00 |
| Pimpinella saxifraga | per multiple flower inflorescence | 122.50000 | NA | 122.50 | 122.50 |
| Pimpinella saxifraga | per ramet/tussock or individual plant | 2264.75000 | 3633.802173 | 268.00 | 9566.00 |
| Pimpinella saxifraga | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Plantago lanceolata | per multiple flower inflorescence | 28.05000 | 4.112988 | 24.00 | 33.60 |
| Plantago lanceolata | per ramet/tussock or individual plant | 2714.16667 | 3586.977023 | 585.00 | 10000.00 |
| Plantago lanceolata | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Plantago lanceolata | unknown | 93.50000 | 47.376154 | 60.00 | 127.00 |
| Plantago major | per multiple flower inflorescence | 2030.00000 | NA | 2030.00 | 2030.00 |
| Plantago major | per ramet/tussock or individual plant | 13835.37500 | 13268.546792 | 565.00 | 40000.00 |
| Plantago major | per single flower inflorescence | 19.00000 | 12.727922 | 10.00 | 28.00 |
| Plantago media | per ramet/tussock or individual plant | 2894.00000 | 3951.519708 | 282.00 | 7440.00 |
| Plantago media | per single flower inflorescence | 4.89000 | 2.983991 | 2.78 | 7.00 |
| Poa annua | per ramet/tussock or individual plant | 3674.20000 | 5415.575057 | 100.00 | 13000.00 |
| Poa annua | per single flower inflorescence | 5071.25000 | 9952.567822 | 50.00 | 20000.00 |
| Poa pratensis | per ramet/tussock or individual plant | 208.00000 | NA | 208.00 | 208.00 |
| Poa trivialis | per ramet/tussock or individual plant | 299.50000 | NA | 299.50 | 299.50 |
| Polygala comosa | per multiple flower stem | 18.00000 | NA | 18.00 | 18.00 |
| Polygala vulgaris | per multiple flower stem | 23.00000 | NA | 23.00 | 23.00 |
| Polygala vulgaris | per ramet/tussock or individual plant | 188.00000 | NA | 188.00 | 188.00 |
| Polygala vulgaris | per single flower inflorescence | 2.00000 | NA | 2.00 | 2.00 |
| Potentilla anserina | per ramet/tussock or individual plant | 69.90000 | 72.235933 | 9.70 | 150.00 |
| Potentilla anserina | per single flower inflorescence | 36.25000 | 19.445436 | 22.50 | 50.00 |
| Primula veris | per ramet/tussock or individual plant | 255.50000 | 21.920310 | 240.00 | 271.00 |
| Primula veris | per single flower inflorescence | 32.10000 | 25.314423 | 14.20 | 50.00 |
| Prunella grandiflora | per multiple flower inflorescence | 58.00000 | NA | 58.00 | 58.00 |
| Prunella vulgaris | per multiple flower inflorescence | 192.00000 | NA | 192.00 | 192.00 |
| Prunella vulgaris | per ramet/tussock or individual plant | 324.93750 | 328.628157 | 43.30 | 854.00 |
| Prunella vulgaris | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Ranunculus bulbosus | per ramet/tussock or individual plant | 70.61818 | 35.252200 | 23.00 | 133.60 |
| Ranunculus bulbosus | per single flower inflorescence | 22.80000 | NA | 22.80 | 22.80 |
| Ranunculus repens | per multiple flower stem | 132.50000 | 10.606602 | 125.00 | 140.00 |
| Ranunculus repens | per ramet/tussock or individual plant | 115.01000 | 62.723475 | 20.00 | 215.30 |
| Ranunculus repens | per single flower inflorescence | 40.00000 | 14.142136 | 30.00 | 50.00 |
| Rhinanthus angustifolius | per ramet/tussock or individual plant | 400.66667 | 428.294719 | 63.50 | 1225.00 |
| Rhinanthus minor | per ramet/tussock or individual plant | 84.02857 | 70.433958 | 19.40 | 187.00 |
| Rhinanthus minor | per single flower inflorescence | 14.16667 | 3.617089 | 10.00 | 16.50 |
| Rumex acetosa | per multiple flower inflorescence | 102.50000 | 34.330841 | 64.50 | 147.50 |
| Rumex acetosa | per ramet/tussock or individual plant | 537.13333 | 778.824000 | 45.40 | 2100.00 |
| Rumex crispus | per ramet/tussock or individual plant | 12122.55556 | 14359.603956 | 1050.00 | 40000.00 |
| Salvia pratensis | per multiple flower stem | 95.00000 | NA | 95.00 | 95.00 |
| Salvia pratensis | per ramet/tussock or individual plant | 3352.50000 | 1771.302487 | 2100.00 | 4605.00 |
| Salvia pratensis | per single flower inflorescence | 19.85000 | 22.415285 | 4.00 | 35.70 |
| Sanguisorba minor | per single flower inflorescence | 3.00000 | NA | 3.00 | 3.00 |
| Scabiosa columbaria | per ramet/tussock or individual plant | 98.00000 | 142.021125 | 24.00 | 311.00 |
| Sedum sexangulare | per multiple flower stem | 148.00000 | NA | 148.00 | 148.00 |
| Senecio erucifolius | per multiple flower stem | 1613.25000 | 653.013112 | 1151.50 | 2075.00 |
| Senecio jacobaea | per multiple flower stem | 4462.50000 | NA | 4462.50 | 4462.50 |
| Senecio jacobaea | per ramet/tussock or individual plant | 56920.00000 | 40538.544621 | 2100.00 | 100000.00 |
| Senecio vulgaris | per multiple flower inflorescence | 101.00000 | NA | 101.00 | 101.00 |
| Senecio vulgaris | per ramet/tussock or individual plant | 8741.16667 | 14552.529408 | 720.00 | 38000.00 |
| Silene dioica | per ramet/tussock or individual plant | 3892.92000 | 4102.989792 | 220.00 | 9300.00 |
| Silene dioica | per single flower inflorescence | 244.00000 | 62.225397 | 200.00 | 288.00 |
| Silene nutans | per ramet/tussock or individual plant | 255.80000 | NA | 255.80 | 255.80 |
| Sonchus asper | per ramet/tussock or individual plant | 47470.53333 | 153108.490438 | 330.00 | 600000.00 |
| Stellaria holostea | per multiple flower stem | 23.37500 | 14.236192 | 13.00 | 44.00 |
| Stellaria holostea | per ramet/tussock or individual plant | 19.00000 | NA | 19.00 | 19.00 |
| Stellaria media | per ramet/tussock or individual plant | 14000.00000 | 1732.050808 | 11000.00 | 15000.00 |
| Stellaria media | per single flower inflorescence | 10.95000 | 4.087787 | 8.00 | 17.00 |
| Symphytum officinale | per multiple flower stem | 72.50000 | NA | 72.50 | 72.50 |
| Symphytum officinale | per ramet/tussock or individual plant | 808.00000 | 837.214429 | 216.00 | 1400.00 |
| Tanacetum vulgare | per multiple flower stem | 232.00000 | NA | 232.00 | 232.00 |
| Tanacetum vulgare | per ramet/tussock or individual plant | 6686.00000 | 6049.897768 | 425.00 | 12500.00 |
| Thymus pulegioides | per multiple flower inflorescence | 33.11000 | NA | 33.11 | 33.11 |
| Thymus pulegioides | per ramet/tussock or individual plant | 528.50000 | NA | 528.50 | 528.50 |
| Thymus pulegioides | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Tragopogon pratensis | per ramet/tussock or individual plant | 190.00000 | NA | 190.00 | 190.00 |
| Trifolium campestre | per multiple flower inflorescence | 29.50000 | NA | 29.50 | 29.50 |
| Trifolium campestre | per ramet/tussock or individual plant | 142.50000 | NA | 142.50 | 142.50 |
| Trifolium campestre | per single flower inflorescence | 1.00000 | NA | 1.00 | 1.00 |
| Trifolium dubium | per ramet/tussock or individual plant | 92.00000 | NA | 92.00 | 92.00 |
| Trifolium dubium | per single flower inflorescence | 1.00000 | NA | 1.00 | 1.00 |
| Trifolium medium | per multiple flower stem | 6.30000 | NA | 6.30 | 6.30 |
| Trifolium medium | per ramet/tussock or individual plant | 70.00000 | 56.568542 | 30.00 | 110.00 |
| Trifolium medium | per single flower inflorescence | 1.00000 | NA | 1.00 | 1.00 |
| Trifolium ochroleucon | per ramet/tussock or individual plant | 275.00000 | NA | 275.00 | 275.00 |
| Trifolium pratense | per multiple flower inflorescence | 65.80000 | 39.173716 | 38.10 | 93.50 |
| Trifolium pratense | per multiple flower stem | 19.80000 | 11.043550 | 11.80 | 32.40 |
| Trifolium pratense | per ramet/tussock or individual plant | 530.00000 | NA | 530.00 | 530.00 |
| Trifolium pratense | per single flower inflorescence | 1.00000 | 0.000000 | 1.00 | 1.00 |
| Trifolium repens | per multiple flower inflorescence | 118.00000 | 110.308658 | 40.00 | 196.00 |
| Trifolium repens | per multiple flower stem | 10.10000 | NA | 10.10 | 10.10 |
| Trifolium repens | per ramet/tussock or individual plant | 421.33333 | 505.732472 | 64.00 | 1000.00 |
| Trifolium repens | per single flower inflorescence | 53.16667 | 83.869442 | 3.50 | 150.00 |
| Trisetum flavescens | per multiple flower stem | 217.50000 | NA | 217.50 | 217.50 |
| Trisetum flavescens | per ramet/tussock or individual plant | 25000.00000 | NA | 25000.00 | 25000.00 |
| Triticum aestivum | per ramet/tussock or individual plant | 471.50000 | NA | 471.50 | 471.50 |
| Valeriana dioica | per ramet/tussock or individual plant | 42.50000 | 24.748737 | 25.00 | 60.00 |
| Valeriana officinalis | per multiple flower stem | 413.00000 | NA | 413.00 | 413.00 |
| Valeriana officinalis | per ramet/tussock or individual plant | 398.00000 | 366.281313 | 139.00 | 657.00 |
| Verbascum lychnitis | per ramet/tussock or individual plant | 109830.25000 | 30574.236558 | 88211.00 | 131449.50 |
| Verbascum lychnitis | per single flower inflorescence | 153.00000 | NA | 153.00 | 153.00 |
| Veronica chamaedrys | per ramet/tussock or individual plant | 59.80000 | 115.081162 | 3.00 | 450.00 |
| Vicia cracca | per multiple flower stem | 66.55000 | 31.890516 | 44.00 | 89.10 |
| Vicia cracca | per ramet/tussock or individual plant | 154.25000 | 104.716681 | 55.00 | 300.00 |
| Vicia cracca | per single flower inflorescence | 5.00000 | 1.414214 | 4.00 | 6.00 |
| Vicia hirsuta | per ramet/tussock or individual plant | 168.48000 | 113.055526 | 12.40 | 300.00 |
| Vicia sepium | per multiple flower stem | 6.10000 | 5.185235 | 1.60 | 13.50 |
| Vicia sepium | per ramet/tussock or individual plant | 76.50000 | 95.459416 | 9.00 | 144.00 |
| Vicia sepium | per single flower inflorescence | 7.00000 | 4.242641 | 4.00 | 10.00 |
| Vicia tetrasperma | per ramet/tussock or individual plant | 280.42000 | 161.453962 | 12.10 | 450.00 |
| Vicia tetrasperma | per single flower inflorescence | 4.00000 | NA | 4.00 | 4.00 |
| Vicia villosa | per ramet/tussock or individual plant | 250.00000 | 50.000000 | 200.00 | 300.00 |
The number of seeds was not measured for the same reproductive unit in all species. To make measures across species as standardized as possible, when available, we gave preferrence to values reported for "ramet/tussock or individual plant" first, "multiple flower inflorescence" or "multiple flower stem" second, "single flower inflorescence" third, and "unknown" at last. Measures of seed numbers taken "per square meter" where excluded because thes could not be approach to individual quantities. Similar to seed mass, the variation in trait values is passed down to the model by inputing minimal and maximal values, from which individual values are drawn, following an Uniform distribution.
seednumber_edm <- bind_rows(filter(seed_number_unit, str_detect(repro_unit, "individual")),
filter(seed_number_unit,
str_detect(repro_unit, "multiple") &
!(sp %in% (filter(seed_number_unit,
str_detect(repro_unit, "individual"))$sp))),
filter(seed_number_unit,
str_detect(repro_unit, "single") &
!(sp %in% (filter(seed_number_unit,
str_detect(repro_unit, "individual") |
str_detect(repro_unit, "multiple"))$sp))),
filter(seed_number_unit,
str_detect(repro_unit, "unknown") &
!(sp %in% (filter(seed_number_unit,
str_detect(repro_unit, "individual") |
str_detect(repro_unit, "multiple") |
str_detect(repro_unit, "single"))$sp))))%>%
left_join(spp_list,.)%>%
dplyr::select(-repro_unit)
seednumber_edmunif <- seednumber_edm %>%
dplyr::select(sp, seednumber_min, seednumber_max)3.3.5 Life span
Species life spans were retrieved from the LEDA database [@kleyerleda2008], and from the BiolFlor database [@klotzbiolflor2002], if not available in LEDA (detailed below). The original, discrete, LEDA classification (van Groenendael, J.M.) is based upon measurements, observations or published data (Table @ref(tab:class-span)). For non-clonal or closely-knitted clones, the lifespan is the number of years of appearance of the adult.
lifespan_leda %>%
mutate(leda_span = str_replace_all(leda_span, pattern = "_", replacement = " ")) %>%
dplyr::select("Life span (LEDA)" = leda_span) %>%
unique %>%
kable(caption = "Categories of life span values reported in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
footnote(alphabet = c("\"Strictly monocarpic bi-annuals\" only reproduce once.",
"In some cases, the same species is classified as both annual and perennial. Mean of span values was taken as the final value."))| Life span (LEDA) |
|---|
| annuals |
| perennials |
| summer annuals |
| winter annuals |
| strict monocarpic bi-annuals and poly-annuals |
| poly-annuals < 5 years (short-lived perennials) |
| poly-annuals > 50 years (long-lived perennials) |
| poly-annuals 5-50 years (medium-lived perennials) |
| a "Strictly monocarpic bi-annuals" only reproduce once. |
| b In some cases, the same species is classified as both annual and perennial. Mean of span values was taken as the final value. |
Quantitative weekly values are necessary to control density-independent mortality during the simulation individuals older than the lifespan are killed deterministically). LEDA's categorical classification of lifespans is converted to weekly values by assigning values falling inside the interval defined by the categories. The variation in trait values is passed down to the model by inputing minimal and maximal values, from which individual values are drawn, following an Uniform distribution.
lifespan <- lifespan_leda %>%
left_join(spp_list,.)%>%
mutate(
span = case_when(
leda_span == "poly-annuals_>_50_years_(long-lived_perennials)" ~ 50*52,
leda_span %in% c("poly-annuals_5-50_years_(medium-lived_perennials)", "perennials") ~ 30*52,
leda_span == "poly-annuals_<_5_years_(short-lived perennials)" ~ 5*52,
leda_span == "strict_monocarpic_bi-annuals_and_poly-annuals" ~ 2*52,
leda_span%in%c("summer_annuals","winter_annuals", "annuals") ~ 1*52))%>%
dplyr::select(sp,span)%>%
group_by(sp)%>%
summarize(span_mean = mean(span),
span_sd = sd(span),
span_min = min(span),
span_max = max(span))%>% # Keep mean values for conflicting values
distinct()%>%
mutate(span_sd = case_when(
is.na(span_sd) & !(is.na(span_mean)) ~ 0.1*span_mean,
TRUE ~ span_sd))
lifespan_edmunif_leda <- lifespan%>%
dplyr::select(sp, span_min, span_max)3.3.5.1 Missing values
Estimates of life span duration are missing for 57 species (Table @ref(tab:nas-spans)).
# save species for which lifespan is missing
nas_spans <- lifespan_edmunif_leda %>%
filter(is.na(span_min), is.na(span_max)) %>%
dplyr::select(sp)
nas_spans %>%
mk_itspp() %>%
kable(caption = "Species for which life span values were missing in the LEDA database.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "400px")| species |
|---|
| Achillea millefolium |
| Acinos arvensis |
| Aegopodium podagraria |
| Allium schoenoprasum |
| Anthriscus sylvestris |
| Arabis hirsuta |
| Artemisia vulgaris |
| Astragalus glycyphyllos |
| Brachypodium pinnatum |
| Briza media |
| Campanula rapunculoides |
| Campanula rotundifolia |
| Carex leporina |
| Carex ornithopoda |
| Centaurea jacea |
| Centaurea scabiosa |
| Cerastium holosteoides |
| Cichorium intybus |
| Cirsium acaule |
| Cirsium oleraceum |
| Clinopodium vulgare |
| Dactylis glomerata |
| Daucus carota |
| Elytrigia repens |
| Epilobium angustifolium |
| Equisetum arvense |
| Festuca pratensis |
| Filipendula vulgaris |
| Fragaria vesca |
| Galium mollugo |
| Galium verum |
| Geum urbanum |
| Helianthemum nummularium |
| Heracleum sphondylium |
| Hieracium lachenalii |
| Hypericum perforatum |
| Inula conyzae |
| Lotus corniculatus |
| Medicago falcata |
| Melampyrum nemorosum |
| Melampyrum sylvaticum |
| Mentha verticillata |
| Myosotis sylvatica |
| Onobrychis viciifolia |
| Ononis spinosa |
| Origanum vulgare |
| Plantago major |
| Plantago media |
| Poa annua |
| Poa trivialis |
| Potentilla neumanniana |
| Senecio ovatus |
| Stachys germanica |
| Tanacetum vulgare |
| Taraxacum laevigatum |
| Taraxacum officinale |
| Veronica teucrium |
The classification of life spans in the BiolFlor database is smaller than in the LEDA database, having three, instead of five classes: annuals, biannuals, and pluriennials. For annuals and biannuals, weekly values are straightforward. For pluriennals, we atributed the same value used for LEDA's "medium-lived" perennials (30 years), to avoid the extremes of that classification, i.e. "short" and "long-lived" perennials.
lifespan_edmunif <- lifespan_biolflor%>%
mutate(
span_min = case_when(
biolflor_span == "annual" ~ 1*52,
biolflor_span == "biannual" ~ 2*52,
str_detect(biolflor_span, "pluriennial-pollakanthic") ~ 30*52),
span_max = span_min)%>%
dplyr::select(-biolflor_span)%>%
bind_rows(filter(lifespan_edmunif_leda, !is.na(span_min), !is.na(span_max)))3.3.6 Clonality
Species clonal ability was retrieved from the LEDA database [@kleyerleda2008], which reports the type of organ structure that enables clonal reproduction. Species that are not in listed in the LEDA database were considered to be non-clonal.
clonality_edm <- clonal_leda%>%
left_join(spp_list,.)%>%
distinct()%>%
mutate(clonality = case_when(
(is.na(organ1) & is.na(organ2) & is.na(organ3) & is.na(organ4)) ~ FALSE,
TRUE ~ TRUE))%>%
dplyr::select(sp, clonality)3.3.7 Age of first flowering
Species age of first flowering were retrieved from the LEDA database [@kleyerleda2008] These values are reported as intervals, but quantitative weekly values are necessary to control juvenile maturation during the simulation and to parameterize the biomass production rate (detailed in Metabolic biological rates section of Implementation verification). Weekly values were taken from an Uniform probability distribution covering the interval reported. If more than one value was reported for the same species, we use the mean value of the randomly assigned precise values. In some instances, this procedure averaged over different intervals. For that reason, if the same interval had been reported multiple times, the repetition was taken into account when calculating the mean.
firstflower_edm <- firstflower_leda %>%
left_join(spp_list,.)%>%
mutate(
firstflower = case_when(
first_flower == "Within_1_year" ~ runif(1,4,52),
first_flower == "Between_1_and_5_years" ~ runif(1,1*52,5*52),
first_flower == "Over_5_years" ~ runif(1,1*52,10*52)))%>%
group_by(sp)%>%
summarize(firstflower_mean = mean(firstflower),
firstflower_sd = sd(firstflower),
firstflower_min = min(firstflower),
firstflower_max = max(firstflower))3.3.7.1 Mismatching values
In some instances there was a conflict between the limit values of age of first flower and duration of life span (age of first flowering > life span). Values of age of first flowering were scaled down to fit inside the reported life span.
firstflower_edmunif <- firstflower_edm%>%
dplyr::select(sp, firstflower_min, firstflower_max)%>%
left_join(lifespan_edmunif)%>%
mutate(firstflower_min =
case_when(
firstflower_min > span_min ~ 0.5*span_min,
TRUE ~ firstflower_min),
firstflower_max =
case_when(
firstflower_max > span_min ~ firstflower_min,
firstflower_max > span_max ~ firstflower_min,
TRUE ~ firstflower_max))%>%
dplyr::select(sp, firstflower_min, firstflower_max)3.3.8 Flower phenology
Flower phenology, i.e. month of beginning and ending of flowering season, was retrieved from the BiolFlor database [@klotzbiolflor2002]. Monthly ordinal values were converted to weekly values to control flowering during the simulation. Any month was assumed to contain 4.5 weeks.
flowerphenology_edm <- flowerphenology_biolflor %>%
mutate(species = str_replace_all(species, pattern = " ", replacement = "_"))%>%
## convert monthly values to weekly values
mutate(floron = round(ceiling((bluehbeginn_monate-1)*4.5), digits = 0),
## add 4 weeks to the ending month to account for the entirety of it
floroff = round(floor((bluehende_monate-1)*4.5 + 4), digits = 0))%>%
dplyr::select(species, floron, floroff)3.3.9 Seed longevity and bank duration
Species dispersal types were retrieved from the LEDA database [@kleyerleda2008]. The LEDA database provides two files on seed (seed_long.txt) and seed bank longevity, (seed*bank.txt). For our purposes, the files are redundant, because seed*bank.txt contains information on more species, including all the ones in seed_long.txt.
seedbank_goet <- seedbank_leda %>%
left_join(spp_list,.)%>%
distinct()%>%
filter(!(is.na(duration)))
seedlongevity_goet <- seedlongevity_leda %>%
left_join(spp_list,.)%>%
distinct()%>%
filter(!(is.na(longevity)))
# check if species in seedlong (n = 23) match the ones in seedbank (n = 46)
all(seedlongevity_goet$sp %in% seedbank_goet$sp)## [1] TRUETherefore, seed*bank.txt is going to be used to assign seed longevity. Nevertheless, only 46 species of the 194 species listed for the calcareous grasslands in Göttingen have information on the duration of the seed-bank. The missing values will be imputed, as described in the next section.
seedbank_edm <- seedbank_leda%>%
left_join(spp_list,.)%>%
distinct()%>%
mutate(duration = case_when(
duration == "transient" ~ runif(n = 1, min = 1, max = 52),
duration == "short-term_persistent" ~ runif(n = 1, min = 1*52, max = 5*52),
duration == "long-term_persistent" ~ runif(n = 1, min = 5*52, max = 10*52)))%>%
group_by(sp)%>%
summarise(bankduration_mean = mean(duration),
bankduration_sd = sd(duration),
bankduration_min = min(duration),
bankduration_max = max(duration))%>%
mutate(bankduration_sd = case_when(
is.na(bankduration_sd) & !(is.na(bankduration_mean)) ~ 0.1*bankduration_mean,
TRUE ~ bankduration_sd))
seedbank_edmunif <- seedbank_edm%>%
dplyr::select(sp, bankduration_min, bankduration_max)3.3.10 Available data
All data retrieved from the LEDA and BiolFlor databases is listed in [Appendix 2][appendix-traitsdatabases].
traitsleda_incomplete_unif <- spp_list%>%
left_join(seednumber_edmunif)%>%
left_join(dispersal_edm)%>%
left_join(seedmass_edm)%>%
left_join(maxmass_edm)%>%
left_join(lifespan_edmunif)%>%
left_join(firstflower_edmunif)%>%
left_join(seedbank_edmunif)%>%
left_join(clonality_edm)%>%
distinct()3.3.11 Imputation of missing trait values
Missing values are handled separately, after all the data available in LEDA and BiolFlor databases has been processed. We performed a multiple imputation of these values [@josse-missMDA]. This procedure is performed by an iterative principal component analysis algorithm (PCA), whereby, starting from a randomly imputed value, missing values are recalculated until the algorithm converges to the principal components of the complete set of variables [@josse-missMDA]. This procedure makes it possible to account for the global similarities between species and correlations between trait values, which can be interpreted as species trait syndromes. Imputed values, however, are not used to calibrate the model.
summary(traitsleda_incomplete_unif)## sp seednumber_min seednumber_max kernel
## Length:194 Min. : 1.00 Min. : 3.0 Length:194
## Class :character 1st Qu.: 41.98 1st Qu.: 191.5 Class :character
## Mode :character Median : 189.00 Median : 500.0 Mode :character
## Mean : 5318.69 Mean : 21135.1
## 3rd Qu.: 538.75 3rd Qu.: 6750.0
## Max. :200000.00 Max. :600000.0
## NA's :70 NA's :70
## seedmass maxmass span_min span_max
## Min. :0.000100 Min. : 25.62 Min. : 52 Min. : 52
## 1st Qu.:0.001000 1st Qu.:118.80 1st Qu.: 104 1st Qu.:1560
## Median :0.001000 Median :118.80 Median :1560 Median :1560
## Mean :0.000821 Mean :100.77 Mean :1128 Mean :1369
## 3rd Qu.:0.001000 3rd Qu.:118.80 3rd Qu.:1560 3rd Qu.:1560
## Max. :0.001000 Max. :118.80 Max. :1560 Max. :2600
## NA's :23 NA's :23
## firstflower_min firstflower_max bankduration_min bankduration_max
## Min. : 18.77 Min. : 18.77 Min. : 3.876 Min. : 3.876
## 1st Qu.: 18.77 1st Qu.: 18.77 1st Qu.: 3.876 1st Qu.: 3.876
## Median : 18.77 Median :105.60 Median : 3.876 Median :149.458
## Mean : 55.89 Mean : 77.84 Mean : 7.111 Mean :155.042
## 3rd Qu.:105.60 3rd Qu.:105.60 3rd Qu.: 3.876 3rd Qu.:385.780
## Max. :310.49 Max. :310.49 Max. :149.458 Max. :385.780
## NA's :38 NA's :38 NA's :149 NA's :149
## clonality
## Mode :logical
## FALSE:43
## TRUE :151
##
##
##
## corleda_unif <- cor(dplyr::select(traitsleda_incomplete_unif, -sp, -kernel, -clonality),
use = "na.or.complete")## Warning in cor(dplyr::select(traitsleda_incomplete_unif, -sp, -kernel, -
## clonality), : the standard deviation is zerocorrplot(corleda_unif, method = "number", type = "upper")
Correlation between the
traitsleda4impute_unif <- as.data.frame(dplyr::select(traitsleda_incomplete_unif,
-sp, -kernel, -clonality))
traitsleda_imput_unif <- as.data.frame(imputePCA(traitsleda4impute_unif,
ncp = 2, scale = TRUE)$completeObs)
summary(traitsleda_imput_unif)## seednumber_min seednumber_max seedmass maxmass
## Min. :-19160.51 Min. :-92587.9 Min. :0.0001000 Min. : 25.62
## 1st Qu.: 19.43 1st Qu.: 112.5 1st Qu.:0.0007891 1st Qu.: 97.47
## Median : 208.75 Median : 688.5 Median :0.0010000 Median :118.80
## Mean : 6012.30 Mean : 22533.2 Mean :0.0008163 Mean :100.28
## 3rd Qu.: 3211.15 3rd Qu.: 14124.8 3rd Qu.:0.0010000 3rd Qu.:118.80
## Max. :200000.00 Max. :600000.0 Max. :0.0010332 Max. :122.38
## span_min span_max firstflower_min firstflower_max
## Min. : 52 Min. : 52 Min. : 6.332 Min. : 9.277
## 1st Qu.: 104 1st Qu.:1560 1st Qu.: 18.773 1st Qu.: 18.773
## Median :1560 Median :1560 Median : 26.000 Median :105.262
## Mean :1128 Mean :1369 Mean : 59.086 Mean : 81.857
## 3rd Qu.:1560 3rd Qu.:1560 3rd Qu.:105.596 3rd Qu.:105.596
## Max. :1560 Max. :2600 Max. :310.487 Max. :310.487
## bankduration_min bankduration_max
## Min. :-16.188 Min. : 3.876
## 1st Qu.: 1.615 1st Qu.:112.824
## Median : 3.876 Median :146.124
## Mean : 6.502 Mean :148.827
## 3rd Qu.: 6.941 3rd Qu.:170.577
## Max. :149.458 Max. :385.7803.3.11.1 Special case: Seed number
Note that the procedure generated negative values for seed number and for seed bank duration.
For the values of seed number, a separate imputation including only values of seed number and seed mass (the trait value mostly correlated to seed number) is able to retain only positive values.
seedleda4impute_unif <- as.data.frame(dplyr::select(traitsleda_incomplete_unif,
seednumber_min,
seednumber_max,
seedmass))
ndim_seed <- estim_ncpPCA(seedleda4impute_unif, scale = TRUE)
seedleda_imput_unif <- as.data.frame(imputePCA(seedleda4impute_unif, ncp = 1, scale = TRUE)$completeObs)
summary(seedleda_imput_unif)## seednumber_min seednumber_max seedmass
## Min. : 1.0 Min. : 3 Min. :0.0001000
## 1st Qu.: 102.0 1st Qu.: 300 1st Qu.:0.0008198
## Median : 673.5 Median : 6832 Median :0.0010000
## Mean : 5889.9 Mean : 22959 Mean :0.0008198
## 3rd Qu.: 2100.0 3rd Qu.: 22219 3rd Qu.:0.0010000
## Max. :200000.0 Max. :600000 Max. :0.0010000For the values of seed-bank duration, however, we could not find other trait values to support multiple imputation. Thus, the mean value of all species was used.
traitsleda_complete_unif <- bind_cols(dplyr::select(traitsleda_incomplete_unif,
c(sp, kernel, clonality, bankduration_min)),
dplyr::select(traitsleda_imput_unif,
c(-seednumber_min,
-seednumber_max,
-bankduration_min)), # imputation puts negative values
dplyr::select(seedleda_imput_unif,
-seedmass))%>%
mutate(bankduration_min = case_when(
is.na(bankduration_min) ~ mean(bankduration_min, na.rm=TRUE),
TRUE ~ bankduration_min))%>%
dplyr::select(sp:clonality, seedmass:bankduration_max, bankduration_min, seednumber_min:seednumber_max)3.3.11.2 Comparison between original and imputed values
bind_rows(dplyr::select(traitsleda_incomplete_unif,
c(seednumber_min, seednumber_max, seedmass, maxmass,
span_min, span_max, firstflower_min, firstflower_max,
bankduration_min, bankduration_max)) %>%
pivot_longer(.,
cols = c(seednumber_min, seednumber_max, seedmass, maxmass,
span_min, span_max, firstflower_min, firstflower_max,
bankduration_min, bankduration_max),
names_to = "trait",
values_to = "value") %>%
mutate(origin = "available"),
dplyr::select(traitsleda_incomplete_unif,
c(seednumber_min, seednumber_max, seedmass, maxmass,
span_min, span_max, firstflower_min, firstflower_max,
bankduration_min, bankduration_max)) %>%
pivot_longer(.,
cols = c(seednumber_min, seednumber_max, seedmass, maxmass,
span_min, span_max, firstflower_min, firstflower_max,
bankduration_min, bankduration_max),
names_to = "trait",
values_to = "value") %>%
mutate(origin = "imputation")) %>%
mutate(trait = recode_factor(trait,
firstflower_min = "Age of first flowering (min.)",
firstflower_max = "Age of first flowering (max.)",
maxmass = "Adult biomass (max.)",
seedmass = "Seed biomass",
bankduration_min = "Seedbank duration (min.)",
bankduration_max = "Seedbank duration (max.)",
seednumber_min = "Seed number (min.)",
seednumber_max = "Seed number (max.)",
span_min = "Life span (min.)",
span_max = "Life span (max.)")) %>%
ggplot(aes(x = value, color = origin, fill = origin)) +
geom_histogram() +
scale_colour_manual(values = viridis(n = 2, alpha = 0.5),
aesthetics = c("colour", "fill"))+
facet_wrap(~trait, nrow = 2, scales = "free") +
labs(colour = "Data origin", fill = "Data origin") +
theme(legend.position = "bottom")## Warning: Removed 1120 rows containing non-finite values (stat_bin).
3.3.12 Metabolic biological rates
The metabolic normalization constants, \(b_{0}\), are taxa and region-dependent values that adapt the allometric body size-rate allometric relationship to different biological rates, for a given set of species in a given set of environmental conditions (@browntoward2004) Therefore, the constants should be calibrated according to a pre-defined list of temperatures and species traits values. Following, we report the calibration process for species present at calcareous grasslands in the vicinity of the city of Göttingen, Germany (@krausshabitat2010).
3.3.12.1 Biomass growth rates
The biomass growth rate is parameterised to reproduce the Richards curve (@richardsflexible1959), also known as generalized logistic function (S-shaped), derived from the Bertalanffy growth function and adapted to plant growth [@richardsflexible1959].
In its original formulation, size (or weight, length, height) at any time (\(l_{t}\)) is defined by \(A\), the lower asymptote; \(L\), the upper assymptote; \(B_{grow}\), the growth rate; \(t_{m}\), the time of maximum growth; and \(Q\), a parameter that affects the point of inflection of the curve. \[l_{t} = A + \frac{L}{(1 + Q*e^{-B_{grow}*(t-t_{m})})^{1/Q}}\]
We adapted plant size traits available in the literature to parameterize this equation and derive growth rates \(B\). We used a plant's seed mass value as the lower asymptote (\(size_{min}\)), and the maximal size as defined by @weissgrazing2014 (and detailed below) as the upper asymptote \(size_{max}\); the age of first flowering (\(age_{f}\)) is set as the the time of maximum growth for all species. Parameter \(Q\) affects the height at which the point of inflection happens, while \(B\) is the rate of growth: \[size = size_{min} + \frac{size_{max}}{(1 + Q*exp(-B_{grow}*(t-age_{1^{st}flower})^{1/Q}}\]
richards5 <- function(age, seedmass, maxmass, Q, firstflower, Bg){
mass = seedmass + maxmass/((1 + Q*exp(-Bg*(c(1:age)-firstflower)))^(1/Q))
return(mass)
}
plot(c(1:52),
richards5(age = 52, seedmass = 10^(-4), maxmass = fullsize(10), Q = 1, firstflower = 26, Bg = 1),
type = "l", lwd = 2,
xlab = "Age (weeks)", ylab = "Size (g)", main = "Richards curve growth for an annual small plant")
lines(c(1:52),
richards5(age = 52, seedmass = 10^(-4), maxmass = fullsize(10), Q = 0.2, firstflower = 26, Bg = 1),
col = "darkorchid1", lwd = 2)
lines(c(1:52),
richards5(age = 52, seedmass = 10^(-4), maxmass = fullsize(10), Q = 5, firstflower = 26, Bg = 1),
col = "deeppink", lwd = 2)
lines(c(1:52),
richards5(age = 52, seedmass = 10^(-4), maxmass = fullsize(10), Q = 5, firstflower = 26, Bg = 2),
col = "goldenrod1", lwd = 2)
lines(c(1:52),
richards5(age = 52, seedmass = 10^(-4), maxmass = fullsize(10), Q = 5, firstflower = 26, Bg = 0.5),
col = "dodgerblue2", lwd = 2)
abline(v = 26, lty = 2) #v at firstflower
legend("bottomright",
legend = c("Q = 1, B = 1", "Q = 0.2, B = 1", "Q = 5, B = 1", "Q = 5, B = 2", "Q = 5, B = 0.5"),
fill = c("black", "darkorchid1", "deeppink", "goldenrod1", "dodgerblue2"),
bty = "n")
Growth calculated for the first time-step is particularly sensitive to the value of \(B_{grow}\). We use \(Q=5\), as it generates higher growth during the juvenile phase of development (before the age of first flowering).
Out of the five parameters used to model the Richards curve, three can be related to plant traits available in the databases we consulted: age of first flowering, and minimal and maximal sizes. We used these three traits to identify "growth groups", i.e. groups of species that should grow between their respective minimal and maximal sizes, within the time frame of the age of first flowering. By reclassfying values of age of first flowering into one month (4 weeks) interval classes and combining it with the three size classes available (@ref(tab:size-classes), we obtained thirteen "growth groups" (each bin in Fig.@ref(fig:growth-groups) is a group).
sizeage.df <- spp_list %>%
left_join(dplyr::select(firstflower_edm, c(sp, firstflower_mean))) %>%
left_join(dplyr::select(lifespan, c(sp, span_mean)), by = "sp") %>%
left_join(dplyr::select(seednumber_edm, c(sp, seednumber_mean)), by = "sp") %>%
left_join(seedmass_edm) %>%
left_join(maxmass_edm) %>%
distinct() %>%
inner_join(weiss_goetmasses, by = "sp") %>%
dplyr::select(seednumber_mean, seedmass, maxmass, span_mean, firstflower_mean)
plot_grid(ggplot(data = sizeage.df,
aes(x = firstflower_mean))+
geom_histogram(binwidth = 4)+ ## one month interval class of age of 1st flower
scale_fill_viridis(discrete = TRUE)+
facet_wrap(~seedmass,
labeller = labeller(seedmass = sizes_facets))+
labs(y = "Count") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 50, vjust = 0.5, size = 9),
axis.text.y = element_text(size = 9),
panel.border = element_rect(colour = "grey77", fill = NA),
legend.position = "none"),
ggplot(data = sizeage.df,
aes(x = firstflower_mean))+
geom_histogram(binwidth = 4)+
theme(axis.text.x = element_text(angle = 50, vjust = 0.5, size = 9),
axis.title.x = element_text(size = 10),
axis.text.y = element_text(size = 9)) +
labs(x = "Age of first flowering (weeks)",
y = "Count"),
ncol = 1, nrow = 2,
rel_heights = c(2, 1),
labels = c("A)", "B)"))## Warning: Removed 5 rows containing non-finite values (stat_bin).
## Warning: Removed 5 rows containing non-finite values (stat_bin).
Growth groups characterized by age of first flowering and species sizes. A) Distribution of values of age of first flowering among species of each size group (small, medium, and large). Each bin covers an interval of 4 weeks (one month). In combination, groups of age of first flowering and groups of size give rise to 13 groups of growth. B) Overall distribution of age of first flowering, showing only eight groups of
Ideally, the growth curve would have its maximal rate of growth at the age of first flowering, and maximal size would be achieved before plants reached their maximal life span. The Richards curves has a parameter to control the first (\(t_{m} = age_{f}\)), but not the latter. Nonetheless, the Richards curve assures that the maximal asymptote (maximal size) is achieved shortly after the point of inflection (\(t_{m} = age_{f}\)).
growthgroups <- sizeage.df %>%
filter(!is.na(firstflower_mean)) %>%
# get the upper values of the first age of flowering bins (discretize() returns the indices of groups, but I need the actual values)
mutate(groupflowering = seq(from = min(.$firstflower_mean, na.rm = TRUE),
to = max(.$firstflower_mean, na.rm = TRUE),
by = 4)[arules::discretize(firstflower_mean,
method = "fixed",
breaks = seq(
from = min(firstflower_mean),
to = max(firstflower_mean)+4,
by = 4),
labels = FALSE)])%>%
dplyr::select(seedmass, maxmass, groupflowering)%>%
distinct()%>%
arrange(maxmass)%>%
mutate(growth_grp = paste("g", row.names(.), sep = ""))%>%
dplyr::select(growth_grp, seedmass, maxmass, groupflowering)
# vector version of the richards growth function
richards5_4plot <- function(seedmass, maxmass, groupflowering){
# constants necessary for the richards curves
Bg = 0.25; Q = 5
growth_span = 3*52
mass = seedmass + maxmass/((1 + Q*exp(-Bg*(c(1:growth_span)-groupflowering)))^(1/Q))
return(data.frame(t=c(1:growth_span),
mass = mass))
}
growth_curves <- apply(dplyr::select(growthgroups, -growth_grp), 1,
function(x)do.call(richards5_4plot, as.list(x)))%>%
setNames(paste("g", seq(1,13), sep = ""))%>%
bind_rows(., .id = "growth_grp") %>%
left_join(growthgroups)%>%
as.data.frame
growth_curves %>%
mutate(groupflowering = round(groupflowering, digits = 0))%>%
ggplot(aes(x = t, y = mass))+
geom_line(aes(color = as.factor(seedmass), group = seedmass))+
geom_vline(aes(xintercept = groupflowering), color = "deeppink")+
geom_hline(aes(yintercept = maxmass), color = "salmon", linetype = "dashed")+
facet_wrap(~groupflowering, nrow = 2,
scales = "free_y",
labeller = labeller(seedmass = sizes_facets))+
labs(x = "Time (weeks)",
y = "Total biomass (g)")+
scale_color_viridis(discrete = TRUE)+
theme(legend.position = "bottom")
Example of Richards curves for the 13 groups of size and ages of first flowering.
3.3.12.2 Calibration of growth normalization constants
The \(B\) value chosen above was fitted to a model (the Richards curve) that assumes continuous growth (constant rate). In the EDM model, however, growth rate depends on environmental temperature and the individual's biomass. For temperature regions, this means that growth is concentrated in warmer months and drastically decreased during colder ones (biomass actually drops due fall frost and winter dieback). When calibrating the growth normalization constant \(b_{0}\), therefore, we must account for the fact that the rate must somehow balance itself during the whole year. Thus, we calibrate it using the year mean temperatures: in higher temperatures, the growth rate increases above the mean, in lower temperatures, it decreases. A caveat of this approach is the assumption of the same growth rate for all species, regardless of age of first flowering or plant size, as in (. This generates different normalization constants according to species sizes. While the proposition of metabolic theory is to use one normalization value per biological rate, variations in this values are expected at small scales [@browntowards2004, @siblymtebook]. Despite this shortcoming, the parameters thus chosen generate growth curves that approximate the Richard curves we indented to reproduce in the first place (Table @ref(tab:growth-pars), Fig. @ref(fig:check-growth-curves)).
growth_pars <- expand.grid(
seedmass = c(0.0001, 0.0003, 0.001),
B = c(0.25))%>%
mutate(b0grow = (B)/((seedmass^(-1/4))*exp(-aE/(Boltz*mean(tempseries_goettingen$meantemp_K)))))%>% #per mass rate, therefore alpha = -1/4
arrange(seedmass)%>%
# add cloumn of max mass
mutate(maxmass = case_when(
seedmass == 0.0001 ~ 2*10+10^(3/4),
seedmass == 0.0003 ~ 2*20+20^(3/4),
seedmass == 0.001 ~ 2*50+50^(3/4)))%>%
mutate(size_label = sizes_facets[as.character(seedmass)])
growth_pars %>%
rename("Seed mass" = "seedmass",
"Growth rate (B)" = "B",
"Normalization constant (b_{0})" = "b0grow",
"Max. size (g)" = "maxmass",
"Size group" = "size_label") %>%
knitr::kable(caption = "Growth parameters derived for the size-based growth groups.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Seed mass | Growth rate (B) | Normalization constant (b_{0}) | Max. size (g) | Size group |
|---|---|---|---|---|
| 0.0001 | 0.25 | 4531446957 | 25.62341 | Small (1g) |
| 0.0003 | 0.25 | 5963719581 | 49.45742 | Medium (2g) |
| 0.0010 | 0.25 | 8058178821 | 118.80302 | Big (5g) |
3.3.13 Species temperature niche (Figure S@ref(fig:sim-ranges))
Species have a temperature niche, defined by temperature optimum and tolerance, which affects species performance as described in Mortality. To define these values, we searched the GBIF database for the species distribution, and the WordClim database for the mean annual temperatures (\(T_{loc_{\mu}}\)), minimal temperature in the coldest month (\(T_{loc_{min}}\)), and maximal temperature in the warmest month (\(T_{loc_{max}}\)) of the retrieved locations.
We used a combination of species name and the taxonomic key, to ensure all occurrence data was retrieved.
species <- spp_list %>%
mutate(sp = str_replace_all(sp, pattern = "_", replacement = " "))%>%
rename(species = sp)
keys <- sapply(species$species, function(x) name_suggest(x)$key[1], USE.NAMES=FALSE) # to check correctedness of the name
keys_names <- sapply(species$species, function(x) name_suggest(x)$key[1],
simplify = TRUE,
USE.NAMES = TRUE) # used to build final table
species_keys <- as_tibble(matrix(unlist(keys_names), nrow = length(unlist(keys_names)),
byrow = TRUE,
dimnames = list(names(unlist(keys_names)), c("key"))),
rownames = "species")
# Search for the species taxonomic key
occurences_keys <- occ_search(taxonKey=keys,
return = "data",
fields = c("decimalLatitude", "decimalLongitude"))
save(occurences_keys,
file = file.path("goetoccurenceGBIF_keys.rda"))
## unlist the occurences
occurences_keys = load(file.path("goetoccurenceGBIF_keys.rda"))
occcoordinates_keys = data.frame()
for(i in seq(occurences_keys)){
if(typeof(occurences_keys[[i]]) == "list" &&
length(occurences_keys[[i]]) > 0 &&
!is.null(keys[[i]]))
{
if(i==1){
occcoordinates_keys = data.frame(key = keys[[i]], occurences_keys[[i]])
}else{
occcoordinates_keys = bind_rows(occcoordinates_keys,
data.frame(key = keys[[i]], occurences_keys[[i]]))
}
}
}
# Search for species scientific name
occurences_scinames <- occ_search(scientificName=species$species, return = "data",
fields=c("decimalLongitude", "decimalLatitude"))
save(occurences_scinames,
file = file.path("goetoccurenceGBIF_scinames.rda"))
## unlist the occurences
occurences_scinames = load(file.path("goetoccurenceGBIF_scinames.rda"))
occcoordinates_scinames = data.frame()
for(i in seq(occurences_scinames)){
if(typeof(occurences_scinames[[i]]) == "list" &&
length(occurences_scinames[[i]]) > 0 &&
!is.null(keys[[i]]))
{
if(i==1){
occcoordinates_scinames = data.frame(key = keys[[i]], occurences_scinames[[i]])
}else{
occcoordinates_scinames = bind_rows(occcoordinates_scinames,
data.frame(key = keys[[i]], occurences_scinames[[i]]))
}
}
}
## Combine both and check for replicates and discrepancies
missingGBIF <- bind_rows(occcoordinates_keys, occcoordinates_cinames)%>%unique()%>%
left_join(species_keys,.)%>%
filter(is.na(decimalLongitude))%>%
filter(is.na(decimalLatitude))
allcoordinates <- bind_rows(occcoordinates_keys, occcoordinates_scinames)%>%
unique()%>%
left_join(species_keys, .)%>%
filter(!is.na(decimalLongitude))%>%
filter(!is.na(decimalLatitude))
raster::getData(name = "worldclim", download = TRUE,
var = "bio",
res = 2.5, ##0.5 did not have enough data
lon = allcoordinates$decimalLongitude,
lat = allcoordinates$decimalLatitude,
path = file.path("worldclim_data", "bioclim"))
##Load data & Extract values
bioclimdata <- raster::getData(name = "worldclim", download = FALSE,
var = "bio",
res = 2.5,
lon = allcoordinates$decimalLongitude,
lat = allcoordinates$decimalLatitude,
path = file.path("worldclim_data/bioclim"))[[c(1,5,6,12,13,14)]]
names(bioclimdata) <- c("tmean", "tmax", "tmin", "prec", "pwet", "pdry")
## extract values
bioclimvars <- raster::extract(bioclimdata,
allcoordinates[c("decimalLongitude", "decimalLatitude")],
df = TRUE)%>%
bind_cols(allcoordinates, .)
## Summarize
bioclimtable <- left_join(species_keys, bioclimvars)%>%
left_join(species, .)%>%
group_by(species)%>%
summarise(tmean_mean = mean((tmean/10)+273.15, na.rm = TRUE),
tmean_sd = sd((tmean/10)+273.15, na.rm = TRUE),
tmax_max = max(tmax, na.rm = TRUE),
tmin_min = min(tmin, na.rm = TRUE),
prec_mean = mean(prec, na.rm = TRUE),
pwet_max = mean(pwet, na.rm = TRUE),
pdry_max = mean(pdry, na.rm = TRUE))
save(occcoordinates_keys,
occcoordinates_scinames,
allcoordinates,
bioclimtable,
bioclimdata,
bioclimvars,
file = file.path(traitsdir, "bioclim.RData"))The species optimum temperature is the mean values of all mean values reported for its locations T_{opt}= \end{equation} where \(n_{loc}\) is the number of locations reported for the distribution of a species.
Following, it is necessary to choose the values that will represent the species range breath as the standard deviation of the Gaussian function (Eq. @ref(eq:fitness). In a Normal distribution, value of standard deviation approximates \(^1/_6\) of the range of the distribution it generates. Thus, we use calculate the species global temperature range \(max(T_{loc_{max}})-min(T_{loc_{min}})\) to calculate its niche breath as \[\begin{equation} T_{tol}=\frac{max(T_{loc_{max}})-min(T_{loc_{min}})}{6}, (\#eq:temp-range) \end{equation}\].
## Summarize temperature values available for the species from Göttingen
species <- spp_list %>%
mutate(sp = str_replace_all(sp, pattern = "_", replacement = " "))%>%
rename(species = sp)
set.seed(100)
bioclim_edm <- left_join(species, bioclimvars)%>%
ungroup() %>%
group_by(species)%>%
## avoid warning from summarize because Festuca Pratensis is missing from rgbif
filter(species != "Festuca pratensis")%>%
summarise(tmean_mean = mean((tmean/10)+273.15, na.rm = TRUE),
tmean_sd = sd((tmean/10)+273.15, na.rm = TRUE),
tmin_min = min((tmin/10)+273.15, na.rm = TRUE),
tmax_max = max((tmax/10)+273.15, na.rm = TRUE))%>%
ungroup()%>%
mutate(temprange = (tmax_max-tmin_min)/6)%>%
## mean value of geographical is the temperature optimum
rename(temp_opt = tmean_mean,
temp_tol = temprange) %>%
## prepare to merge with trait's list
mutate(species = str_replace_all(species, pattern = " ", replacement = "_"))
write_csv(bioclim_edm, path = file.path(traitsdir, "bioclim_edm.csv"))The minimal and maximal values generated match most of the observed values (Fig. @ref(fig:sim-ranges)). For comparison, we show the values generated when using the \(sd(T_{loc_{\mu})\). Table (@ref(tab:bioclim-edm-range) in Appendix 2 shows the simulated temperature range for each species.
bioclim_ranges.df <- bioclim_edm.df%>%
rowwise()%>%
mutate(simsd_min = min(rnorm(n=1000, mean=.$temp_opt, sd=.$tmean_sd)),
simsd_max = max(rnorm(n=1000, mean=.$temp_opt, sd=.$tmean_sd)),
simrange_min = min(rnorm(n=1000, mean=.$temp_opt, sd=.$temp_tol)),
simrange_max = max(rnorm(n=1000, mean=.$temp_opt, sd=.$temp_tol))) %>%
rename(observed_min = tmin_min, observed_max = tmax_max)%>%
dplyr::select(species,
## observed values
observed_min, observed_max,
## values simulated with sd of mean temperatures
simsd_min, simsd_max,
## values simulated with sd = range/6
simrange_min, simrange_max)
bioclim_ranges.df %>%
pivot_longer(cols = c(observed_min, observed_max,
simsd_min, simsd_max,
simrange_min, simrange_max),
names_to = "source",
values_to = "temp") %>%
separate(col = source, into = c("Source", "range"), sep = "_") %>%
mutate(Source = recode_factor(Source,
"observed" = "Observed",
"simrange" = "Simulated from range",
"simsd" = "Simulated from mean")) %>%
ggplot(aes(x = temp))+
labs(x = "Temperature (K)", y = "Density")+
geom_density(aes(colour = Source, fill = Source, group = Source), alpha = .5)+
scale_colour_viridis_d() +
scale_fill_viridis_d()+
theme(legend.position = "bottom")
Comparison of the minimal and maximal values retrieved from rgbif and WordClim (green), the values generated from the distribution based on the species global temperature range (purple), and the values generated from the distribution based on the mean value of the species distribution (purple)
3.3.14 Germination & Mortality
From the allometry study reported by @marbaalometric2007, we were able to derive a normalization constant for germination, \(b_{0}^{germ}\), and for mortality, \(b_0^{mort}\).
marba_T <- mean(marba$temp, na.rm = TRUE) # temperature values used in the allometric study by Marbà et al (2007)
b0germ <- (8e-4)/exp(-aE/(Boltz*marba_T)) # value derived from Marbà et al (2007)
b0mort <- 7*(9e-4)/exp(-aE/(Boltz*marba_T)) #value derived from Marbà et al (2007), multiplied to weekly basisThe germination and mortality rates generated by these constant are reported in the [#germiation-mortality-verification] section of the Implementation verification.
4 Implementation verification
Summary The model relies on a series of probability functions that have been parameterized to yield values inside biologically realistic ranges for dispersal kernels, as well as growth, mortality and germination rates. These values are verified in here.
4.1 Dispersal kernels
Verify the density distribution of distances generated by each dispersal kernel.
4.2 Biomass growth
To verify the growth curve yielded by our model, we simulate the growth curve of seeds belonging to each of the size group (small, medium,and large), during a 10 year period, for the temperature series used in [parameterization][#temperature-timeseries]. This simplified simulation includes the loss of biomass during winter dieback, which is not taken into account by the calculation of the S-shaped curve alone.
# temporal markers
ten_years = 10*52 # duration in weeks
spring_time = 13 # week of beginning of spring in temperate climate
minisim_time = seq(spring_time,spring_time+ten_years) # duration of verification realized growth
realized_growth = data.frame(time = minisim_time)
# simplified version of edm's biomass growth submodel
for(seedweight in c(growth_pars$seedmass)){
# table
growth = data.frame(time = minisim_time, size = c(seedweight))
# store values of the realized rate of growth
B <- c()
for(t in 2:length(minisim_time)){
if(minisim_time[t]%%52 == 51){ # winter dieback on the first week
growth[t, "size"] = (2/3)*growth[t-1, "size"]
}else{
pars <- filter(growth_pars, seedmass == seedweight)
Temp <- tempseries_goettingen$meantemp_K[spring_time + t]
Bg = pars$b0grow * growth[t-1, "size"]^(-1/4) * exp(-aE/(Boltz*Temp))
B[t-1] = Bg
growth[t, "size"] = growth[t-1, "size"] + Bg*(pars$maxmass - growth[t-1, "size"])
}
}
realized_growth <- inner_join(realized_growth, growth)%>%
rename_at(vars(grep("size", names(.))), ~dplyr::select(filter(growth_pars, seedmass == seedweight), size_label)[[1]])
}
realgrowth_plot <- realized_growth%>%
gather(key = "size", value = "mass", -time)%>%
mutate(years = time/52) %>%
ggplot(aes(x = years, y = mass, color = size, group = size))+
geom_line()+
scale_color_viridis(discrete = TRUE)+
labs(y = "Weight (g)", color = "Size")+
theme(axis.title.x = element_blank())
tempgrowth <- tempseries_goettingen%>%
filter(week >= spring_time & week <= spring_time + ten_years) %>%
mutate(years = week/52) %>%
dplyr::select(years, meantemp) %>%
ggplot(aes(x = years, y = meantemp))+
geom_line(colour = "grey")+
labs(x = "Time (years)", y = "Temperature (°C)")
plot_grid(realgrowth_plot + theme(legend.position = "bottom"), tempgrowth,
align = "v", ncol = 1,
rel_heights = c(2,1),
labels = c("A)", "B)"))
Simulated realized growth for each growth group, using the rates defined in Table ref(tab:growth-pars).
4.3 Germination and mortality rates
## data frame holding info on sizes
small <- c(0.0001,(2*10+10^(3/4))/2,2*10+10^(3/4))
medium <- c(0.0003,(2*20+20^(3/4))/2,2*20+20^(3/4))
large <- c(0.001,(2*50+50^(3/4))/2,2*50+50^(3/4))
stage <- as.factor(c("seed","juv","adult"))
stageweights <- data.frame(sp = rep(c("small","medium","large"), each =3),
stage = rep(stage,3),
weight = c(small,medium,large))
# Germination rate
germ_verif.plot <- filter(stageweights, stage == "seed")%>%
mutate(max = b0germ*(weight^(-1/4))*exp(-aE/(Boltz*max(tempseries_goettingen$meantemp_K))),
min = b0germ*(weight^(-1/4))*exp(-aE/(Boltz*min(tempseries_goettingen$meantemp_K)))) %>%
gather(key = temp, value = rate, min:max) %>%
mutate(probability = 1 - exp(-rate)) %>%
mutate(sp = recode_factor(sp,
small = "Small",
medium = "Medium",
large = "Large")) %>%
ggplot(aes(x = sp, y = probability, fill = as.factor(temp)))+
geom_bar(stat = "identity", position = position_dodge(1))+
scale_fill_viridis(discrete = TRUE) +
labs(x = "Size group", y = "Probability", fill = "Temperature") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 20),
legend.position = "bottom")
## Mortality rate
mort_verif.plot <- data.frame(stage = rep(c("juvmin", "juvmax"),3),
weight = c(0.0001, (2*10+10^(3/4))*0.7,
0.0003, (2*20+20^(3/4))*0.7,
0.001, (2*50+50^(3/4))*0.7),
sp = rep(c("small", "medium", "large"),each = 2)) %>%
rbind(stageweights,.) %>%
filter(stage != "juv") %>%
arrange(sp, stage) %>%
mutate(min = b0mort*(weight^(-1/4))*exp(-aE/(Boltz*min(tempseries_goettingen$meantemp_K))),# max. temperature
max = b0mort*(weight^(-1/4))*exp(-aE/(Boltz*max(tempseries_goettingen$meantemp_K)))) %>%
gather(key = temp, value = rate, min:max) %>%
mutate(probability = 1 - exp(-rate)) %>%
mutate(sp = recode_factor(sp,
small = "Small",
medium = "Medium",
large = "Large")) %>%
mutate(stage = recode_factor(stage,
seed = "Seed",
juvmin = "Juv. min.",
juvmax = "Juv. max.",
adult = "Adult")) %>%
ggplot(aes(x = stage, y = probability, fill = as.factor(temp))) +
geom_bar(stat = "identity", position = position_dodge(1)) +
scale_fill_viridis(discrete = TRUE) +
facet_wrap(~sp) +
labs(x = "Stage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 20),
axis.title.y = element_blank())
plot_grid(plot_grid(germ_verif.plot + theme(legend.position = "none"),
mort_verif.plot + theme(legend.position = "none"),
nrow = 1, rel_widths = c(1,2), labels = c("A)", "B)")),
cowplot::get_legend(germ_verif.plot),
nrow = 2, rel_heights = c(6,1))
Germination (A) and mortality (B) rates yielded by the model, for the three biomass groups, at the minimum and maximal temperatures used for calibration. Mortality rates (B) are also distinguished among different stages, because they differ in biomass, and thus, in the resulting metabolic rate as well. In that regard, "Juv. min." refers to juveniles minimal biomass (the same as seeds), and "Juv. max.", to juveniles maximal biomass, which approaches 70% of the adult's maximal biomass, according to the Richards growth curve.
4.4 Fitness performance
Verifying limit values of fitness
# fitness = c()
# for(T in tempseries_goettingen$meantemp_K){
# for(sp in 1:length(spptraits_unif)){
# fitness = c(fitness,1*exp(-((T-spptraits_unif$temp_opt[sp])^2)/(2*(spptraits_unif$temp_tol[sp]^2))))
# }
# }
# min(fitness)
# max(fitness)5 Model output verification
Summary: In this section, we report how much model outputs match observations following the parameterization and inverted callibration reported in Data evaluation.
5.1 Species trait space
Being a trait-based model, one of the most important feats of the model is reproducing the trait space of the real-world communities the simulations are set up to reproduce. To verify it, we performed a Principal Component Analysis of the trait spaces of simulated communities with the trait space of species reported by Krauss et al. (2004) for 31 calcareous grasslands in the vicinity of the city of Göttingen (Germany). We first compared the trait space of simulated communities in the control scenarios and communities reported in patches of areas smaller than 484 m² and 961 m².
## calculate relative covers (sums are not always 100)
dens_rltv.df <- dens_raw.df %>%
inner_join(., dplyr::select(denssumm_raw.df, c(fragment, Grosse))) %>%
rename("size" = Grosse,
"cover" = Deckungsgrad) %>%
mutate_at(vars(-c(fragment, size, cover)), function(x) x/.$cover) %>%
arrange(size)
## gather trait information
traits_frags.df <- dens_rltv.df%>%
pivot_longer(cols = -c(fragment, size, cover), #names(.)[2:193],
names_to = "species", values_to = "rltv_cover") %>%
## join spp traits
inner_join(.,edm_traits) %>%
mutate(kernel = case_when(
kernel == "long-short" ~ "short-long",
kernel == "long-medium" ~ "medium-long",
kernel == "medium-short" ~ "short-medium",
TRUE ~ kernel)) %>%
## calculate mean values from input boundaries
group_by(fragment, species) %>%
mutate(span = mean(runif(n = 1000, min = span_min, max = span_max)),
firstflower = mean(runif(n = 1000,
min = firstflower_min, max = firstflower_max)),
bankduration = mean(runif(n = 1000,
min = bankduration_min, max = bankduration_max)),
seednumber = mean(runif(n = 1000,
min = seednumber_min, max = seednumber_max))) %>%
dplyr::select(-c(compartsize, contains("max"), contains("min")))
traits_frags.pcadf <- traits_frags.df %>%
filter(rltv_cover > 0) %>%
## summarize per fragment ~ replicate of model experiments
group_by(fragment, size, kernel, clonality, pollen_vector, self_failoutcross) %>%
summarize_at(vars(bankduration, firstflower, floron, floroff,
seedon, seedoff, seednumber, seedmass, self_proba, span),
~weighted.mean(., rltv_cover)) %>%
ungroup %>%
## summarize categorical variables in relative proportions per replicate
mutate(clonality = as.character(clonality),
self_failoutcross = as.character(self_failoutcross)) %>%
pivot_longer(cols = c(kernel, clonality, pollen_vector, self_failoutcross),
names_to = "trait", values_to = "value") %>%
group_by(fragment, size, trait, value) %>%
unique %>%
mutate(n = n()) %>%
ungroup %>%
group_by(fragment, size, trait) %>%
mutate(freq = n/sum(n)) %>%
ungroup %>%
dplyr::select(-n) %>%
rowwise() %>%
mutate(trait_value = paste(trait, value, sep = "_")) %>%
dplyr::select(-c(trait, value)) %>%
pivot_wider(names_from = trait_value, values_from = freq) %>%
mutate_at(vars(contains("kernel"), contains("clonality"), contains("pollen_vector"),
contains("self_failoutcross")),
funs(ifelse(is.na(.), 0, .))) %>%
mutate(origin = "data")5.1.1 Simulated and real-world trait spaces (Figures @ref(fig:frags-pcarep)-@ref(fig:large-frags-pcarep))
frags.pcadf <- traits_frags.pcadf %>%
filter(size <= 1000) %>% ## includes both fragments sizes
bind_rows(scen_wgtmean.df %>%
filter(disturbance == "Control", initial_area == 500, year == 160) %>%
dplyr::select(-c(year, rplkt, disturbance, intensity, initial_area)) %>%
mutate(origin = "simulations"))
frags.pcalist <- frags.pcadf %>%
dplyr::select(-c(fragment, size, origin)) %>%
get_pcaoutputs(.,traitnames.dict)## Warning in PCA(tab4pca, scale.unit = TRUE, graph = FALSE): Missing values are
## imputed by the mean of the variable: you should use the imputePCA function of
## the missMDA package(frags.biplot <- fviz_pca_biplot(frags.pcalist$pca,
geom.ind = "point",
col.ind = as.character(frags.pcadf$origin),
addEllipses = TRUE,
col.var = "grey27",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Trait space") +
scale_colour_manual(values = rev(viridis(n = 2, option = "D")),
aesthetics = c("colour", "fill")) +
theme(legend.position = "bottom"))
Analysis of principal components of the trait space of all communities simulated (dark purple) and the trait space estimated for real-world communities(yellow). Out of the 24 variables used to define the trait space, only the 12 most contributing ones were included, to facilitate visualization.
plot_grid(frags.pcalist$corrvars.plot + theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
frags.pcalist$scree.plot + theme(plot.title = element_blank()),
frags.pcalist$contribvars.barplot + theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space of simulated communities (484 m² and 961 m²) and the trait space estimated for real-world communities (smaller than 1000m²): A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
smallfrags.pcadf <- traits_frags.pcadf %>%
filter(size <= 500) %>%
bind_rows(scen_wgtmean.df %>%
filter(disturbance == "Control", initial_area == 500, year == 160) %>%
dplyr::select(-c(year, rplkt, disturbance, intensity, initial_area)) %>%
mutate(origin = "simulations"))
smallfrags.pcalist <- smallfrags.pcadf %>%
dplyr::select(-c(fragment, size, origin)) %>%
get_pcaoutputs(.,traitnames.dict)## Warning in PCA(tab4pca, scale.unit = TRUE, graph = FALSE): Missing values are
## imputed by the mean of the variable: you should use the imputePCA function of
## the missMDA package(smallfrags.biplot <- fviz_pca_biplot(smallfrags.pcalist$pca,
geom.ind = "point",
col.ind = as.character(smallfrags.pcadf$origin),
addEllipses = TRUE,
col.var = "grey27",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Trait space") +
scale_colour_manual(values = rev(viridis(n = 2, option = "D")),
aesthetics = c("colour", "fill")) +
theme(legend.position = "bottom"))
Analysis of principal components of the trait space of a community simulated in a landscape of 484 m² (dark purple) and the trait space estimated for real-world communities(yellow). Out of the 24 variables used to define the trait space, only the 12 most contributing ones were included, to facilitate visualization.
plot_grid(smallfrags.pcalist$corrvars.plot + theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
smallfrags.pcalist$scree.plot + theme(plot.title = element_blank()),
smallfrags.pcalist$contribvars.barplot +
theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space of simulated small communities (484 m²) and the trait space estimated for real-world communities (smaller than 500 m²): A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
largefrags.pcadf <- traits_frags.pcadf %>%
filter(size > 500, size <= 1000) %>%
bind_rows(scen_wgtmean.df %>%
filter(disturbance == "Control", initial_area == 1000, year == 160) %>%
dplyr::select(-c(year, rplkt, disturbance, intensity, initial_area)) %>%
mutate(origin = "simulations"))
largefrags.pcalist <- largefrags.pcadf %>%
dplyr::select(-c(fragment, size, origin)) %>%
get_pcaoutputs(.,traitnames.dict)## Warning in PCA(tab4pca, scale.unit = TRUE, graph = FALSE): Missing values are
## imputed by the mean of the variable: you should use the imputePCA function of
## the missMDA package(largefrags.biplot <- fviz_pca_biplot(largefrags.pcalist$pca,
geom.ind = "point",
col.ind = as.character(largefrags.pcadf$origin),
addEllipses = TRUE,
col.var = "grey27",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Trait space") +
scale_colour_manual(values = rev(viridis(n = 2, option = "D")),
aesthetics = c("colour", "fill")) +
theme(legend.position = "bottom"))
Analysis of principal components of the trait space of a community simulated in a landscape of 961 m² (dark purple) and the trait space estimated for real-world communities(yellow). Out of the 24 variables used to define the trait space, only the 12 most contributing ones were included, to facilitate visualization.
plot_grid(largefrags.pcalist$corrvars.plot + theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
largefrags.pcalist$scree.plot + theme(plot.title = element_blank()),
largefrags.pcalist$contribvars.barplot +
theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space of simulated communities (961 m²) and the trait space estimated for real-world communities (bigger than 500 m² and smaller than 1000 m²): A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
5.1.2 Effect of habitat area decrease on trait space (Figures @ref(fig:data-area-pca)-@ref(fig:areapollloss-pcarep))
empirical_frags.pcadf <- traits_frags.pcadf %>%
filter(size %in% c(quantile(traits_frags.pcadf$size,
probs = c(0.25, 0.5, 0.75, 0.9))[[1]],
quantile(traits_frags.pcadf$size,
probs = c(0.25, 0.5, 0.75, 0.9))[[2]],
quantile(traits_frags.pcadf$size,
probs = c(0.25, 0.5, 0.75, 0.9))[[3]],
quantile(traits_frags.pcadf$size,
probs = c(0.25, 0.5, 0.75, 0.9))[[4]]))
empirical_fragsareas.pcalist <- empirical_frags.pcadf %>%
dplyr::select(-c(fragment, size, origin)) %>%
get_pcaoutputs(.,traitnames.dict)
(fragsarea.biplot <- fviz_pca_biplot(empirical_fragsareas.pcalist$pca,
geom.ind = "none",
col.ind = empirical_frags.pcadf$size,
addEllipses = TRUE,
col.var = "chocolate3",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Size (m²)") +
scale_colour_manual(values = rev(viridis(n = 4, option = "D")),
aesthetics = c("colour", "fill")))
Analysis of principal components of A) the trait space estimated for empirical communities sampled in calcareous grasslands of sizes falling into the 25th, 50th, 75th, 90th percentiles of the areas reported in Krauss et al. (2004).
plot_grid(empirical_fragsareas.pcalist$corrvars.plot +
theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
empirical_fragsareas.pcalist$scree.plot +
theme(plot.title = element_blank()),
empirical_fragsareas.pcalist$contribvars.barplot +
theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space estimated for empirical communities sampled in calcareous grasslands of sizes falling into the 25th, 50th, 75th, 90th percentiles of the areas reported in Krauss et al. (2004): A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
sims_area.pcadf <- scen_wgtmean.df %>%
filter(disturbance == "Area loss") %>%
filter(year == 160)
sims_areas.pcalist <- sims_area.pcadf %>%
dplyr::select(-c(year, rplkt, disturbance, intensity, initial_area)) %>%
get_pcaoutputs(.,traitnames.dict)
(sims_areas.biplot <- fviz_pca_biplot(sims_areas.pcalist$pca,
geom.ind = "none",
col.ind = sims_area.pcadf$intensity,
addEllipses = TRUE,
col.var = "chocolate3",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Intensity of loss (%)") +
scale_colour_manual(values = viridis(n = 4, option = "D"),
aesthetics = c("colour", "fill")) +
theme(legend.position = "bottom"))
Analysis of principal components of A) the trait space estimated for the surviving communities in simulated communities under 25%, 50%, 75%, and 90% of loss of habitat area.
plot_grid(sims_areas.pcalist$corrvars.plot + theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
sims_areas.pcalist$scree.plot + theme(plot.title = element_blank()),
sims_areas.pcalist$contribvars.barplot +
theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space estimated for surviving communities in simulated communities under 25%, 50%, 75%, and 90% of loss of habitat area: A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
sims_areapoll.pcadf <- scen_wgtmean.df %>%
filter(disturbance == "Area and pollination loss") %>%
filter(year == 160)
sims_areaspoll.pcalist <- sims_areapoll.pcadf %>%
dplyr::select(-c(year, rplkt, disturbance, intensity, initial_area)) %>%
get_pcaoutputs(.,traitnames.dict)
sims_areaspoll.biplot <- fviz_pca_biplot(sims_areaspoll.pcalist$pca,
geom.ind = "none",
col.ind = sims_areapoll.pcadf$intensity,
addEllipses = TRUE,
col.var = "chocolate3",
## select 12 most contributing variables to show
select.var = list(contrib = 12),
repel = TRUE,
title = NULL,
legend.title = "Intensity of loss (%)") +
scale_colour_manual(values = viridis(n = 4, option = "D"),
aesthetics = c("colour", "fill")) +
theme(legend.position = "bottom")plot_grid(sims_areaspoll.pcalist$corrvars.plot + theme(axis.text = element_text(size = 11),
axis.title = element_blank()),
sims_areaspoll.pcalist$scree.plot + theme(plot.title = element_blank()),
sims_areaspoll.pcalist$contribvars.barplot +
theme(axis.text = element_text(size = 11),
plot.title = element_blank()),
ncol = 1, labels = c("A)", "B)", "C)"))
Representation of variables in the analysis of principal components (PCA) of the trait space estimated for surviving communities in simulated communities under 25%, 50%, 75%, and 90% of loss of habitat area and pollination services: A) Correlation between traits and the principal components defined by the PCA, B) scree plot showing percentage of variance explained by each principal component defined by the PCA, and C) percentage of contribution of each trait to the first two principal components.
5.2 Species rank abundances
5.3 Species-area accumulation curve
5.4 Species composition
6 Appendix 1: Built-in raster generator
Raster files must always represent the same area as the total initial landscape area.
7 Appendix 2: Trait values available in databases (Table @ref(tab:traits-databases))
traitsleda_incomplete_unif %>%
rename("species" = "sp") %>%
left_join(., flowerphenology_edm) %>%
left_join(., pollination) %>%
inner_join(., dplyr::select(bioclim_edm.df, c(species, temp_opt,temp_tol))) %>%
mk_itspp() %>%
rename("Species" = "species",
"Kernel" = "kernel",
"Clonality" = "clonality",
"Pollen vector" = "pollen_vector",
"Prob. selfing" = "self_proba",
"Selfing" = "selfing_at_failoutcross",
"Seed mass" = "seedmass",
"Max. adult mass" = "maxmass",
"Prob. selfing" = "self_proba",
"Min. life span" = "span_min",
"Max. life span" = "span_max",
"Min. age 1st flower" = "firstflower_min",
"Max. age 1st flower" = "firstflower_max",
"Max. seed bank" = "bankduration_max",
"Min. seed bank" = "bankduration_min",
"Min. seeds" = "seednumber_min",
"Max. seeds" = "seednumber_max",
"Begin flowering" = "floron",
"End flowering" = "floroff",
"Temp. optimum" = "temp_opt",
"Temp. tolerance" = "temp_tol") %>%
knitr::kable(caption = "Trait valeus filtered from the LEDA and BiolFlor databases, for plant species listed for fragments of calcareous grasslands around the city of Göttingen.",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "700px")| Species | Min. seeds | Max. seeds | Kernel | Seed mass | Max. adult mass | Min. life span | Max. life span | Min. age 1st flower | Max. age 1st flower | Min. seed bank | Max. seed bank | Clonality | Begin flowering | End flowering | Pollen vector | Prob. selfing | Selfing | Temp. optimum | Temp. tolerance |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Achillea millefolium | 190.0 | 7200.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | FALSE | 285.6251 | 9.566667 |
| Acinos arvensis | 342.0 | 342.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | 3.875541 | 385.780391 | TRUE | 23 | 40 | insects | 0.000 | FALSE | 278.8466 | 7.666667 |
| Actaea spicata | 108.0 | 1900.0 | long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | TRUE | 18 | 26 | insects | 0.000 | TRUE | 278.4126 | 7.933333 |
| Aegopodium podagraria | 46.5 | 10700.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 385.780391 | TRUE | 23 | 31 | insects | 0.000 | FALSE | 280.6411 | 9.116667 |
| Agrimonia eupatoria | 66.0 | 66.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 149.458251 | TRUE | 23 | 40 | insects | 0.950 | FALSE | 280.4205 | 8.950000 |
| Ajuga genevensis | 221.0 | 221.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 385.780391 | TRUE | 14 | 26 | insects | 0.000 | TRUE | 281.5398 | 8.000000 |
| Ajuga reptans | 36.0 | 105.0 | short-medium | 0.0010 | 118.80302 | 1560 | 2600 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.000 | TRUE | 283.3404 | 8.083333 |
| Allium schoenoprasum | 4149.0 | 4149.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | TRUE | 280.3545 | 9.183333 |
| Alopecurus pratensis | 414.0 | 414.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 26 | wind | 0.000 | FALSE | 280.7221 | 8.766667 |
| Anchusa arvensis | 128.0 | 700.0 | long-medium | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | 3.875541 | 149.458251 | FALSE | 18 | 40 | insects | 0.000 | TRUE | 281.2234 | 9.566667 |
| Antennaria dioica | 280.0 | 280.0 | long-short | 0.0001 | 25.62341 | 1560 | 2600 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | TRUE | 18 | 26 | insects | 0.000 | FALSE | 278.2855 | 8.700000 |
| Anthoxanthum odoratum | 108.0 | 1038.5 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 281.2262 | 7.800000 |
| Anthriscus sylvestris | 132.1 | 10000.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.000 | FALSE | 281.5486 | 7.483333 |
| Anthyllis vulneraria | 30.0 | 194.0 | long | 0.0010 | 118.80302 | 52 | 1560 | 26.00000 | 26.00000 | 3.875541 | 385.780391 | TRUE | 18 | 35 | insects | 0.500 | FALSE | 280.7263 | 8.266667 |
| Arabis hirsuta | 750.0 | 750.0 | long-short | 0.0001 | 25.62341 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 31 | insects | 0.500 | FALSE | 280.1182 | 8.333333 |
| Arrhenatherum elatius | 10.0 | 10.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 44 | wind | 0.050 | FALSE | 281.2127 | 7.400000 |
| Artemisia vulgaris | 200000.0 | 500000.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 27 | 49 | insects | 0.000 | FALSE | 283.0275 | 9.566667 |
| Astragalus glycyphyllos | 10315.0 | 10315.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 31 | insects | 0.000 | FALSE | 279.8322 | 8.416667 |
| Avenula pubescens | 4000.0 | 4000.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 26 | wind | 0.000 | FALSE | 279.8673 | 7.916667 |
| Brachypodium pinnatum | 87.0 | 87.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | wind | 0.000 | FALSE | 281.0725 | 9.000000 |
| Bellis perennis | 1288.0 | 1288.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 0 | 49 | insects | 0.500 | FALSE | 283.4846 | 7.416667 |
| Briza media | 179.0 | 7308.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 26 | wind | 0.000 | FALSE | 279.9232 | 8.733333 |
| Bromus erectus | NA | NA | long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 44 | wind | 0.000 | FALSE | 281.7318 | 6.133333 |
| Bromus hordeaceus | NA | NA | long | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 35 | wind | 0.950 | FALSE | 282.5224 | 8.016667 |
| Calystegia sepium | NA | NA | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 385.780391 | TRUE | 23 | 40 | insects | 0.000 | TRUE | 282.3955 | 9.350000 |
| Campanula glomerata | NA | NA | short-long | 0.0003 | 49.45742 | 52 | 1560 | 26.00000 | 26.00000 | 3.875541 | 149.458251 | TRUE | 23 | 40 | insects | 0.000 | TRUE | 277.7940 | 11.166667 |
| Campanula persicifolia | NA | NA | long-short | 0.0001 | 25.62341 | 104 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | FALSE | 279.8406 | 7.600000 |
| Campanula rotundifolia | NA | NA | long-short | 0.0001 | 25.62341 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | FALSE | 279.3996 | 9.083333 |
| Campanula rapunculoides | NA | NA | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | FALSE | 279.5601 | 9.683333 |
| Cardaminopsis arenosa | NA | NA | long | 0.0003 | 49.45742 | 52 | 1560 | 18.77277 | 18.77277 | 149.458251 | 149.458251 | TRUE | 14 | 35 | insects | 0.000 | FALSE | 280.3846 | 7.900000 |
| Carduus crispus | NA | NA | any | 0.0010 | 118.80302 | 104 | 104 | 52.00000 | 52.00000 | 3.875541 | 385.780391 | FALSE | 27 | 40 | insects | 0.000 | FALSE | 279.9315 | 8.850000 |
| Carex caryophyllea | NA | NA | medium | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 14 | 22 | wind | 0.000 | FALSE | 280.5223 | 8.466667 |
| Carex flacca | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 31 | wind | 0.000 | FALSE | 281.1753 | 7.533333 |
| Carex leporina | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 23 | 31 | wind | 0.000 | FALSE | 279.1100 | 7.100000 |
| Carex ornithopoda | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 18 | 22 | wind | 0.000 | FALSE | 278.5075 | 7.466667 |
| Carlina vulgaris | NA | NA | any | NA | NA | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | TRUE | 280.7739 | 6.816667 |
| Centaurea jacea | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | FALSE | 23 | 49 | insects | 0.000 | FALSE | 280.4447 | 8.633333 |
| Centaurea scabiosa | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 27 | 35 | insects | 0.000 | FALSE | 279.9491 | 9.300000 |
| Centaurium erythraea | NA | NA | short-long | 0.0001 | 25.62341 | 52 | 1560 | NA | NA | NA | NA | FALSE | 27 | 35 | insects | 0.950 | FALSE | 284.0660 | 9.666667 |
| Cephalanthera rubra | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | insects | 0.500 | FALSE | 281.1061 | 8.700000 |
| Cerastium arvense | NA | NA | long-short | 0.0003 | 49.45742 | 1560 | 2600 | 18.77277 | 105.59588 | NA | NA | TRUE | 14 | 31 | insects | 0.000 | TRUE | 281.0167 | 9.916667 |
| Cerastium holosteoides | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 9 | 26 | insects | 0.000 | TRUE | 281.2091 | 7.183333 |
| Cerastium tomentosum | NA | NA | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 31 | insects | 0.000 | TRUE | 281.0699 | 7.233333 |
| Cerinthe minor | NA | NA | short-long | NA | NA | 104 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 31 | insects | 0.000 | TRUE | 280.6468 | 7.683333 |
| Chaerophyllum hirsutum | NA | NA | short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 280.2983 | 6.316667 |
| Chaerophyllum temulum | NA | NA | short-long | 0.0010 | 118.80302 | 52 | 104 | NA | NA | 3.875541 | 149.458251 | FALSE | 18 | 31 | insects | 0.000 | FALSE | 282.3427 | 6.933333 |
| Cichorium intybus | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 149.458251 | TRUE | 27 | 44 | insects | 0.000 | FALSE | 283.5332 | 9.600000 |
| Cirsium acaule | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 280.5920 | 8.450000 |
| Cirsium arvense | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 281.8333 | 9.083333 |
| Cirsium oleraceum | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | FALSE | 280.1936 | 8.366667 |
| Cirsium tuberosum | NA | NA | any | NA | NA | 1560 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 27 | 35 | insects | 0.000 | FALSE | 282.1654 | 6.016667 |
| Cirsium vulgare | NA | NA | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 40 | insects | 0.000 | FALSE | 283.7613 | 8.616667 |
| Clinopodium vulgare | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 280.3037 | 9.183333 |
| Convolvulus arvensis | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 40 | insects | 0.500 | FALSE | 284.1778 | 10.683333 |
| Crepis biennis | NA | NA | any | 0.0010 | 118.80302 | 104 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 35 | insects | 0.000 | TRUE | 281.6483 | 6.333333 |
| Cruciata laevipes | NA | NA | long | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 14 | 26 | insects | 0.000 | FALSE | 282.3399 | 9.150000 |
| Cynosurus cristatus | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 31 | wind | 0.500 | FALSE | 281.0622 | 7.016667 |
| Dactylis glomerata | NA | NA | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 31 | wind | 0.050 | TRUE | 282.0465 | 8.083333 |
| Daucus carota | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 40 | insects | 0.050 | TRUE | 285.5320 | 8.650000 |
| Echium vulgare | NA | NA | any | 0.0010 | 118.80302 | 104 | 104 | 18.77277 | 18.77277 | 3.875541 | 385.780391 | FALSE | 18 | 31 | insects | 0.000 | FALSE | 282.0942 | 9.166667 |
| Elytrigia repens | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 23 | 35 | wind | 0.000 | FALSE | 279.0669 | 6.900000 |
| Epilobium angustifolium | NA | NA | any | 0.0001 | 25.62341 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 27 | 35 | insects | 0.050 | FALSE | 279.1531 | 10.716667 |
| Epilobium montanum | NA | NA | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 40 | insects | 0.950 | FALSE | 279.5080 | 7.866667 |
| Equisetum arvense | NA | NA | any | 0.0001 | 25.62341 | 1560 | 1560 | NA | NA | NA | NA | FALSE | 9 | 17 | wind | 0.500 | FALSE | 281.6585 | 10.416667 |
| Euphorbia cyparissias | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 14 | 22 | insects | 0.050 | FALSE | 282.5568 | 7.733333 |
| Euphrasia officinalis | NA | NA | any | 0.0003 | 49.45742 | 52 | 52 | 18.77277 | 18.77277 | 3.875541 | 149.458251 | FALSE | 18 | 44 | insects | 0.000 | TRUE | 279.2117 | 8.166667 |
| Falcaria vulgaris | 2000.0 | 2000.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 26.00000 | 26.00000 | 3.875541 | 3.875541 | TRUE | 27 | 40 | insects | 0.000 | FALSE | 281.5397 | 8.166667 |
| Festuca ovina | 847.0 | 847.0 | long-short | 0.0010 | 118.80302 | 104 | 1560 | 52.00000 | 52.00000 | NA | NA | TRUE | 18 | 35 | wind | 0.000 | FALSE | 279.3919 | 7.416667 |
| Festuca rubra | 465.0 | 465.0 | any | 0.0010 | 118.80302 | 104 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | wind | 0.000 | FALSE | 279.8448 | 7.500000 |
| Filipendula vulgaris | 113.0 | 439.3 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | TRUE | 23 | 31 | insects | 0.000 | FALSE | 280.5264 | 8.516667 |
| Fragaria vesca | 225.0 | 341.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | TRUE | 282.1414 | 9.266667 |
| Fragaria viridis | NA | NA | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 310.48690 | 310.48690 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 278.8633 | 9.366667 |
| Galeopsis ladanum | 300.0 | 300.0 | long-medium | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 44 | insects | 0.000 | TRUE | 279.7941 | 9.166667 |
| Galium aparine | 31.0 | 1000.0 | long | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 44 | insects | 0.950 | FALSE | 287.8325 | 8.166667 |
| Galium mollugo | 1734.0 | 20000.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 31 | insects | 0.050 | FALSE | 280.4929 | 9.533333 |
| Galium pumilum | NA | NA | short-long | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 27 | 40 | insects | 0.050 | FALSE | 281.4022 | 6.766667 |
| Galium verum | 12.6 | 104.1 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 40 | wind-insects | 0.025 | FALSE | 280.0749 | 9.583333 |
| Genista tinctoria | NA | NA | short-long | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | 3.875541 | 3.875541 | TRUE | 23 | 35 | insects | 0.000 | FALSE | 280.7595 | 8.600000 |
| Gentianella ciliata | 670.0 | 670.0 | short | 0.0003 | 49.45742 | 104 | 1560 | 52.00000 | 52.00000 | 3.875541 | 3.875541 | TRUE | 32 | 44 | insects | 0.050 | FALSE | 280.3280 | 6.650000 |
| Gentianella germanica | 160.0 | 594.0 | short | 0.0003 | 49.45742 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 44 | insects | 0.000 | TRUE | 280.6513 | 6.450000 |
| Geranium dissectum | 95.0 | 1000.0 | long-short | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 35 | insects | 0.000 | TRUE | 285.2910 | 7.200000 |
| Geranium molle | 81.0 | 100.0 | long-short | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | 3.875541 | 385.780391 | FALSE | 18 | 44 | insects | 0.950 | FALSE | 284.3925 | 7.650000 |
| Geranium pratense | 3.0 | 3.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 279.9600 | 8.816667 |
| Geranium pusillum | 300.0 | 750.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 44 | insects | 0.950 | FALSE | 280.8284 | 8.250000 |
| Geranium robertianum | 30.0 | 300.0 | any | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 44 | insects | 0.950 | FALSE | 282.1439 | 7.633333 |
| Geum urbanum | 172.0 | 324.5 | long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 44 | insects | 0.950 | FALSE | 281.6276 | 8.733333 |
| Gymnadenia conopsea | 31699.0 | 31699.0 | short | 0.0001 | 25.62341 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | TRUE | 18 | 35 | insects | 0.000 | FALSE | 278.6253 | 8.683333 |
| Helianthemum nummularium | 243.0 | 243.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | TRUE | 280.1858 | 6.466667 |
| Heracleum sphondylium | 850.0 | 10005.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | TRUE | 283.5358 | 9.316667 |
| Hieracium caespitosum | 612.0 | 612.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 18 | 35 | insects | 0.050 | FALSE | 281.5453 | 9.933333 |
| Hieracium lachenalii | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 23 | 35 | insects | 0.050 | FALSE | 280.7973 | 7.550000 |
| Hieracium murorum | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 18 | 35 | insects | 0.050 | FALSE | 280.0843 | 7.833333 |
| Hieracium pilosella | 21.0 | 75.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 44 | insects | 0.050 | FALSE | 282.3518 | 5.733333 |
| Hippocrepis comosa | 120.0 | 120.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 31 | insects | 0.000 | FALSE | 281.8446 | 6.633333 |
| Holcus lanatus | 177000.0 | 177000.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 35 | wind | 0.500 | FALSE | 282.6703 | 6.850000 |
| Hypericum perforatum | 3036.0 | 33000.0 | any | 0.0001 | 25.62341 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 35 | insects | 0.050 | FALSE | 282.1337 | 9.766667 |
| Inula conyzae | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 23 | 44 | insects | 0.000 | FALSE | 282.6778 | 5.883333 |
| Knautia arvensis | 66.0 | 2000.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 310.48690 | NA | NA | TRUE | 27 | 35 | insects | 0.000 | FALSE | 280.4889 | 8.700000 |
| Koeleria pyramidata | 209.5 | 209.5 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | wind | 0.000 | FALSE | 281.7134 | 6.966667 |
| Lathyrus pratensis | 38.0 | 300.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 279.4419 | 8.583333 |
| Leontodon hispidus | 66.0 | 71.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | FALSE | 280.8981 | 7.983333 |
| Leucanthemum vulgare | 66.1 | 26000.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | TRUE | 282.6034 | 8.516667 |
| Linum catharticum | 11.4 | 183.0 | any | 0.0003 | 49.45742 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 31 | insects | 0.950 | FALSE | 280.3002 | 6.500000 |
| Listera ovata | 21000.0 | 150000.0 | short | NA | NA | 1560 | 1560 | 105.59588 | 310.48690 | 3.875541 | 3.875541 | TRUE | 18 | 26 | insects | 0.050 | FALSE | 280.2161 | 7.016667 |
| Lolium perenne | NA | NA | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 44 | wind | 0.000 | FALSE | 286.1423 | 9.400000 |
| Lotus corniculatus | 345.0 | 18000.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 281.5991 | 8.900000 |
| Medicago falcata | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 23 | 40 | insects | 0.000 | FALSE | 278.8608 | 11.066667 |
| Medicago lupulina | 100.0 | 6600.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 44 | insects | 0.500 | FALSE | 287.8328 | 8.783333 |
| Melampyrum arvense | 10.0 | 40.0 | long-medium | NA | NA | 52 | 104 | 18.77277 | 18.77277 | 3.875541 | 149.458251 | FALSE | 23 | 40 | insects | 0.000 | FALSE | 281.3895 | 8.366667 |
| Melampyrum nemorosum | 12.0 | 438.0 | medium | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | FALSE | 18 | 40 | insects | 0.000 | TRUE | 279.1104 | 8.633333 |
| Melampyrum sylvaticum | 9.4 | 76.0 | medium-long | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | FALSE | 23 | 40 | insects | 0.000 | TRUE | 278.0563 | 7.166667 |
| Melilotus alba | 4287.5 | 350000.0 | long-short | 0.0010 | 118.80302 | 52 | 104 | 26.00000 | 26.00000 | NA | NA | FALSE | 23 | 40 | insects | 0.000 | TRUE | 283.4032 | 9.966667 |
| Melilotus officinalis | 100.0 | 100000.0 | any | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 14 | 40 | insects | 0.000 | TRUE | 282.2428 | 10.850000 |
| Mentha arvensis | 19.5 | 200.0 | long | 0.0003 | 49.45742 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | FALSE | 278.4783 | 10.400000 |
| Mentha verticillata | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 27 | 35 | insects | 0.000 | FALSE | 280.1657 | 8.066667 |
| Myosotis arvensis | 500.0 | 1400.0 | any | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 31 | insects | 0.950 | FALSE | 280.5512 | 7.983333 |
| Myosotis sylvatica | 192.0 | 192.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | FALSE | 18 | 31 | insects | 0.950 | FALSE | 280.8248 | 8.733333 |
| Onobrychis viciifolia | 16.0 | 16.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 18 | 31 | insects | 0.050 | FALSE | 281.8257 | 9.950000 |
| Ononis repens | 1.0 | 4.0 | short-medium | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 31 | insects | 0.000 | FALSE | 282.5768 | 6.550000 |
| Ononis spinosa | 300.0 | 300.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 23 | 31 | insects | 0.000 | FALSE | 281.1944 | 8.716667 |
| Ophrys apifera | 40000.0 | 40000.0 | short | NA | NA | 1560 | 1560 | 105.59588 | 310.48690 | NA | NA | TRUE | 18 | 26 | insects | 1.000 | FALSE | 283.7225 | 6.833333 |
| Ophrys insectifera | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 281.0236 | 7.016667 |
| Orchis mascula | NA | NA | short | NA | NA | 1560 | 1560 | 310.48690 | 310.48690 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 281.6205 | 6.533333 |
| Orchis militaris | NA | NA | short | 0.0001 | 25.62341 | 1560 | 1560 | 105.59588 | 310.48690 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 281.0057 | 9.166667 |
| Orchis purpurea | 6000.0 | 6000.0 | short | NA | NA | 1560 | 1560 | 310.48690 | 310.48690 | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 283.7867 | 6.683333 |
| Orchis tridentata | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 288.6735 | 8.050000 |
| Origanum vulgare | 4.0 | 4.0 | short-long | 0.0001 | 25.62341 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 280.4336 | 9.783333 |
| Phalaris arundinacea | 3000.0 | 3000.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 31 | wind | 0.000 | TRUE | 280.5887 | 9.783333 |
| Phleum pratense | 677.0 | 677.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 35 | wind | 0.025 | FALSE | 280.0307 | 10.500000 |
| Pimpinella saxifraga | 268.0 | 9566.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 279.1678 | 8.283333 |
| Plantago lanceolata | 585.0 | 10000.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 44 | wind | 0.000 | TRUE | 284.8469 | 8.533333 |
| Plantago major | 565.0 | 40000.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 44 | wind | 0.000 | TRUE | 283.9908 | 9.583333 |
| Plantago media | 282.0 | 7440.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 40 | wind-insects | 0.000 | FALSE | 279.5996 | 9.233333 |
| Platanthera chlorantha | NA | NA | short | 0.0001 | 25.62341 | 1560 | 1560 | 310.48690 | 310.48690 | 3.875541 | 3.875541 | TRUE | 18 | 31 | insects | 0.000 | FALSE | 280.1140 | 7.016667 |
| Poa annua | 100.0 | 13000.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 0 | 53 | wind | 0.050 | TRUE | 284.4375 | 9.450000 |
| Poa pratensis | 208.0 | 208.0 | long-short | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 26 | wind | 0.000 | TRUE | 280.0435 | 9.683333 |
| Poa trivialis | 299.5 | 299.5 | any | 0.0003 | 49.45742 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | wind | 0.000 | FALSE | 280.7038 | 8.733333 |
| Polygala comosa | 18.0 | 18.0 | medium-short | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | 3.875541 | 385.780391 | TRUE | 18 | 26 | insects | 0.000 | TRUE | 280.1609 | 7.466667 |
| Polygala vulgaris | 188.0 | 188.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.000 | TRUE | 279.9018 | 7.200000 |
| Potentilla anserina | 9.7 | 150.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.000 | FALSE | 281.3451 | 9.083333 |
| Potentilla neumanniana | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 14 | 26 | insects | 0.950 | FALSE | 283.0755 | 6.250000 |
| Potentilla reptans | NA | NA | any | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 281.6585 | 7.933333 |
| Primula veris | 240.0 | 271.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 14 | 26 | insects | 0.000 | FALSE | 281.5454 | 8.500000 |
| Prunella grandiflora | 58.0 | 58.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 280.7381 | 7.566667 |
| Prunella vulgaris | 43.3 | 854.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | TRUE | 285.1315 | 9.600000 |
| Ranunculus bulbosus | 23.0 | 133.6 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 31 | insects | 0.000 | FALSE | 281.8991 | 7.416667 |
| Ranunculus repens | 20.0 | 215.3 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 35 | insects | 0.000 | TRUE | 282.5288 | 8.483333 |
| Rhinanthus angustifolius | 63.5 | 1225.0 | long-short | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 40 | insects | 0.000 | FALSE | 281.7180 | 6.650000 |
| Rhinanthus minor | 19.4 | 187.0 | long-short | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 40 | insects | 0.100 | FALSE | 279.8482 | 10.266667 |
| Rumex acetosa | 45.4 | 2100.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 31 | wind | 0.000 | FALSE | 280.8967 | 7.966667 |
| Rumex crispus | 1050.0 | 40000.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 35 | wind | 0.000 | FALSE | 287.1484 | 10.533333 |
| Salvia pratensis | 2100.0 | 4605.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.050 | TRUE | 282.3332 | 7.583333 |
| Sanguisorba minor | 3.0 | 3.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 35 | wind | 0.000 | TRUE | 282.4030 | 8.150000 |
| Scabiosa columbaria | 24.0 | 311.0 | any | 0.0010 | 118.80302 | 104 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 27 | 49 | insects | 0.000 | FALSE | 281.7792 | 6.800000 |
| Sedum sexangulare | 148.0 | 148.0 | any | 0.0001 | 25.62341 | 1560 | 1560 | NA | NA | 3.875541 | 385.780391 | TRUE | 23 | 35 | insects | 0.000 | TRUE | 281.2399 | 8.216667 |
| Senecio erucifolius | 1151.5 | 2075.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 385.780391 | TRUE | 27 | 40 | insects | 0.000 | FALSE | 281.9572 | 7.933333 |
| Senecio jacobaea | 2100.0 | 100000.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 282.1799 | 5.783333 |
| Senecio ovatus | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 27 | 40 | insects | 0.000 | FALSE | 280.0713 | 6.733333 |
| Senecio vulgaris | 720.0 | 38000.0 | any | 0.0003 | 49.45742 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | FALSE | 5 | 49 | insects | 0.950 | FALSE | 285.8002 | 8.933333 |
| Silene dioica | 220.0 | 9300.0 | short-long | 0.0010 | 118.80302 | 104 | 1560 | 52.00000 | 52.00000 | NA | NA | TRUE | 18 | 40 | insects | 0.000 | FALSE | 282.0698 | 7.383333 |
| Silene nutans | 255.8 | 255.8 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 18 | 35 | insects | 0.050 | FALSE | 279.9228 | 8.933333 |
| Sonchus asper | 330.0 | 600000.0 | any | 0.0003 | 49.45742 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 44 | insects | 0.500 | FALSE | 289.9740 | 10.116667 |
| Stachys germanica | NA | NA | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 284.6420 | 6.983333 |
| Stellaria holostea | 19.0 | 19.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 14 | 22 | insects | 0.000 | TRUE | 282.0927 | 7.083333 |
| Stellaria media | 11000.0 | 15000.0 | any | 0.0010 | 118.80302 | 52 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 0 | 53 | insects | 0.500 | FALSE | 285.8061 | 7.783333 |
| Symphytum officinale | 216.0 | 1400.0 | medium-long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | 3.875541 | 3.875541 | TRUE | 18 | 31 | insects | 0.000 | TRUE | 282.1790 | 8.050000 |
| Tanacetum vulgare | 425.0 | 12500.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 27 | 40 | insects | 0.000 | FALSE | 280.7742 | 8.800000 |
| Taraxacum laevigatum | NA | NA | any | NA | NA | 1560 | 1560 | NA | NA | NA | NA | FALSE | 14 | 26 | insects | 0.000 | FALSE | 284.2226 | 8.533333 |
| Taraxacum officinale | NA | NA | short | NA | NA | 1560 | 1560 | NA | NA | NA | NA | TRUE | 9 | 44 | insects | 0.000 | FALSE | 287.2466 | 8.450000 |
| Thymus pulegioides | 528.5 | 528.5 | any | 0.0003 | 49.45742 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 44 | insects | 0.000 | FALSE | 280.7396 | 8.850000 |
| Tragopogon pratensis | 190.0 | 190.0 | any | 0.0010 | 118.80302 | 104 | 1560 | 18.77277 | 18.77277 | 3.875541 | 149.458251 | TRUE | 18 | 31 | insects | 0.000 | TRUE | 280.7897 | 8.950000 |
| Trifolium campestre | 142.5 | 142.5 | long-short | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | TRUE | 284.8373 | 8.033333 |
| Trifolium dubium | 92.0 | 92.0 | any | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | NA | NA | FALSE | 18 | 40 | insects | 0.500 | FALSE | 284.5877 | 7.466667 |
| Trifolium medium | 30.0 | 110.0 | long-short | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | 3.875541 | 3.875541 | TRUE | 23 | 35 | insects | 0.000 | FALSE | 279.1436 | 8.416667 |
| Trifolium ochroleucon | 275.0 | 275.0 | long | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 23 | 31 | insects | 0.000 | FALSE | 283.1174 | 6.900000 |
| Trifolium pratense | 530.0 | 530.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 105.59588 | NA | NA | TRUE | 23 | 40 | insects | 0.000 | FALSE | 284.0001 | 8.933333 |
| Trifolium repens | 64.0 | 1000.0 | any | 0.0010 | 118.80302 | 52 | 2600 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 40 | insects | 0.000 | FALSE | 289.0059 | 8.066667 |
| Trisetum flavescens | 25000.0 | 25000.0 | long-short | 0.0003 | 49.45742 | 1560 | 1560 | 18.77277 | 18.77277 | NA | NA | TRUE | 18 | 26 | wind | 0.000 | FALSE | 281.7958 | 7.300000 |
| Triticum aestivum | 471.5 | 471.5 | long | 0.0010 | 118.80302 | 52 | 52 | 18.77277 | 18.77277 | NA | NA | TRUE | 23 | 31 | wind | 0.050 | FALSE | 281.4158 | 10.666667 |
| Valeriana dioica | 25.0 | 60.0 | short | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 18 | 26 | insects | 0.000 | FALSE | 280.8733 | 7.266667 |
| Valeriana officinalis | 139.0 | 657.0 | long | 0.0010 | 118.80302 | 1560 | 1560 | 18.77277 | 18.77277 | 3.875541 | 3.875541 | TRUE | 18 | 35 | insects | 0.000 | FALSE | 279.3240 | 9.350000 |
| Verbascum lychnitis | 88211.0 | 131449.5 | long-short | 0.0003 | 49.45742 | 104 | 104 | 52.00000 | 52.00000 | 3.875541 | 385.780391 | FALSE | 23 | 35 | insects | 0.000 | TRUE | 280.6361 | 9.366667 |
| Veronica chamaedrys | 3.0 | 450.0 | any | 0.0003 | 49.45742 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 18 | 31 | insects | 0.050 | FALSE | 281.1476 | 8.550000 |
| Veronica teucrium | NA | NA | long | 0.0003 | 49.45742 | 1560 | 1560 | NA | NA | NA | NA | FALSE | 18 | 31 | insects | 0.500 | FALSE | 280.8409 | 8.700000 |
| Vicia cracca | 55.0 | 300.0 | any | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | NA | NA | TRUE | 23 | 35 | insects | 0.000 | FALSE | 280.1077 | 10.083333 |
| Vicia hirsuta | 12.4 | 300.0 | any | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 31 | insects | 0.500 | FALSE | 281.6091 | 9.166667 |
| Vicia sepium | 9.0 | 144.0 | short-long | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 385.780391 | TRUE | 18 | 26 | insects | 0.050 | FALSE | 280.6096 | 7.916667 |
| Vicia tenuifolia | NA | NA | short | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | 3.875541 | 385.780391 | TRUE | 23 | 35 | insects | 0.000 | FALSE | 280.9342 | 8.766667 |
| Vicia tetrasperma | 12.1 | 450.0 | any | 0.0010 | 118.80302 | 52 | 104 | 18.77277 | 18.77277 | NA | NA | FALSE | 23 | 31 | insects | 0.950 | FALSE | 280.3759 | 8.600000 |
| Vicia villosa | 200.0 | 300.0 | short | NA | NA | 52 | 104 | 18.77277 | 18.77277 | 3.875541 | 149.458251 | FALSE | 23 | 40 | insects | 0.000 | FALSE | 288.8241 | 6.783333 |
| Vincetoxicum hirundinaria | NA | NA | short | 0.0010 | 118.80302 | 1560 | 1560 | 105.59588 | 105.59588 | 3.875541 | 3.875541 | TRUE | 18 | 35 | insects | 0.000 | FALSE | 280.5451 | 8.816667 |
| Viola hirta | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 14 | 22 | insects | 0.050 | FALSE | 279.2736 | 8.350000 |
| Viola odorata | NA | NA | any | 0.0010 | 118.80302 | 1560 | 1560 | NA | NA | NA | NA | TRUE | 9 | 17 | insects | 0.050 | FALSE | 282.3458 | 7.800000 |
8 Appendix 3: Temperature niches (Table @ref(tab:bioclim-edm-range))
bioclim_ranges.df %>%
mk_itspp() %>%
rename("Species" = "species",
"Min. observed" = "observed_min",
"Max. observed" = "observed_max",
"Min. simulated from range" = "simrange_min",
"Max. simulated from range" = "simrange_max",
"Min. simulated from mean" = "simsd_min",
"Max. simulated from mean" = "simsd_max") %>%
knitr::kable(caption = "Trait values parameterized for the species simulated in the model.",
longtable = TRUE, booktabs = TRUE) %>%
scroll_box(width = "100%", height = "700px")| Species | Min. observed | Max. observed | Min. simulated from mean | Max. simulated from mean | Min. simulated from range | Max. simulated from range |
|---|---|---|---|---|---|---|
| Achillea millefolium | 252.35 | 309.75 | 273.6247 | 298.6337 | 257.1091 | 315.5626 |
| Acinos arvensis | 259.05 | 305.05 | 273.3140 | 297.4421 | 249.0040 | 309.3647 |
| Actaea spicata | 251.35 | 298.95 | 271.5733 | 297.4515 | 247.2507 | 313.2254 |
| Aegopodium podagraria | 251.45 | 306.15 | 273.1025 | 296.9661 | 255.6090 | 314.8608 |
| Agrimonia eupatoria | 249.55 | 303.25 | 271.3894 | 300.2729 | 244.0023 | 310.0338 |
| Ajuga genevensis | 255.55 | 303.55 | 272.3586 | 300.3631 | 255.7053 | 314.6448 |
| Ajuga reptans | 260.25 | 308.75 | 272.2172 | 297.5726 | 245.8194 | 306.6639 |
| Allium schoenoprasum | 250.95 | 306.05 | 270.5958 | 298.1052 | 251.8107 | 314.6026 |
| Alopecurus pratensis | 251.45 | 304.05 | 272.5292 | 297.0672 | 249.8398 | 311.2308 |
| Anchusa arvensis | 246.45 | 303.85 | 270.4272 | 300.1302 | 249.3134 | 313.7804 |
| Antennaria dioica | 251.35 | 303.55 | 272.4692 | 295.7781 | 246.1211 | 314.8801 |
| Anthoxanthum odoratum | 258.25 | 305.05 | 269.3261 | 301.3453 | 255.3709 | 315.2348 |
| Anthriscus sylvestris | 257.85 | 302.75 | 272.6774 | 296.3065 | 246.7877 | 313.6718 |
| Anthyllis vulneraria | 256.55 | 306.15 | 270.9947 | 296.2885 | 252.6090 | 306.0738 |
| Arabis hirsuta | 253.55 | 303.55 | 271.7456 | 296.1269 | 248.4404 | 307.5185 |
| Arrhenatherum elatius | 259.45 | 303.85 | 272.4873 | 301.2551 | 254.3652 | 310.2552 |
| Artemisia vulgaris | 248.45 | 305.85 | 270.6277 | 297.5117 | 252.5611 | 309.0698 |
| Astragalus glycyphyllos | 251.25 | 301.75 | 273.6807 | 299.6455 | 254.1326 | 311.7298 |
| Avenula pubescens | 251.25 | 298.75 | 270.5607 | 300.3666 | 252.2766 | 310.9778 |
| Bellis perennis | 264.25 | 308.75 | 272.7270 | 297.3808 | 256.8908 | 314.9438 |
| Brachypodium pinnatum | 251.35 | 305.35 | 271.8935 | 296.3392 | 245.4206 | 309.2055 |
| Briza media | 256.25 | 308.65 | 269.6428 | 296.5647 | 250.0233 | 306.6226 |
| Bromus erectus | 265.25 | 302.05 | 270.7743 | 300.1759 | 248.8886 | 314.6488 |
| Bromus hordeaceus | 260.55 | 308.65 | 271.5119 | 296.5298 | 247.2014 | 309.4756 |
| Calystegia sepium | 251.35 | 307.45 | 272.3342 | 297.9916 | 257.3362 | 312.7455 |
| Campanula glomerata | 237.35 | 304.35 | 273.9455 | 299.5868 | 253.9283 | 314.0526 |
| Campanula persicifolia | 256.45 | 302.05 | 272.0643 | 299.6875 | 253.4816 | 304.6597 |
| Campanula rapunculoides | 247.95 | 306.05 | 270.2812 | 300.4276 | 251.0822 | 313.1695 |
| Campanula rotundifolia | 249.05 | 303.55 | 273.1328 | 299.8341 | 245.6465 | 306.4821 |
| Cardaminopsis arenosa | 252.55 | 299.95 | 273.1531 | 297.1440 | 253.3199 | 308.3215 |
| Carduus crispus | 247.45 | 300.55 | 271.3290 | 296.3783 | 254.2110 | 320.2363 |
| Carex caryophyllea | 252.75 | 303.55 | 270.5881 | 302.7003 | 253.5952 | 310.5639 |
| Carex flacca | 259.65 | 304.85 | 271.8919 | 297.7684 | 252.2577 | 308.2003 |
| Carex leporina | 257.35 | 299.95 | 267.3812 | 295.2055 | 246.6678 | 314.7086 |
| Carex ornithopoda | 255.75 | 300.55 | 270.2505 | 298.7570 | 258.1217 | 308.9113 |
| Carlina vulgaris | 260.35 | 301.25 | 272.9122 | 297.5253 | 250.9794 | 309.3439 |
| Centaurea jacea | 251.35 | 303.15 | 269.9800 | 297.4345 | 250.8622 | 315.3595 |
| Centaurea scabiosa | 247.05 | 302.85 | 270.5578 | 295.3047 | 249.8257 | 311.5345 |
| Centaurium erythraea | 251.35 | 309.35 | 272.2132 | 296.7777 | 259.1210 | 308.9173 |
| Cephalanthera rubra | 253.25 | 305.45 | 270.8743 | 300.3383 | 250.6122 | 308.3046 |
| Cerastium arvense | 248.05 | 307.55 | 272.4741 | 298.6873 | 253.1285 | 310.7252 |
| Cerastium holosteoides | 259.45 | 302.55 | 271.2406 | 297.4218 | 249.8849 | 307.7074 |
| Cerastium tomentosum | 258.35 | 301.75 | 272.7797 | 297.6906 | 253.8947 | 307.8256 |
| Cerinthe minor | 257.25 | 303.35 | 271.8416 | 296.6455 | 246.3815 | 317.8059 |
| Chaerophyllum hirsutum | 260.45 | 298.35 | 272.4356 | 295.4367 | 248.4829 | 312.2812 |
| Chaerophyllum temulum | 260.95 | 302.55 | 272.7020 | 296.9326 | 250.9165 | 308.1723 |
| Cichorium intybus | 251.35 | 308.95 | 271.5317 | 298.2910 | 250.0809 | 312.0060 |
| Cirsium acaule | 249.45 | 300.15 | 272.7582 | 300.2076 | 256.4536 | 313.0571 |
| Cirsium arvense | 251.35 | 305.85 | 271.3738 | 298.9929 | 257.2942 | 318.2104 |
| Cirsium oleraceum | 249.45 | 299.65 | 270.8214 | 295.9075 | 254.5082 | 311.6002 |
| Cirsium tuberosum | 266.15 | 302.25 | 269.3461 | 303.8182 | 249.5312 | 314.0168 |
| Cirsium vulgare | 255.95 | 307.65 | 271.6066 | 294.6168 | 255.0246 | 313.0821 |
| Clinopodium vulgare | 250.55 | 305.65 | 271.1728 | 298.0468 | 252.6103 | 312.4178 |
| Convolvulus arvensis | 250.85 | 314.95 | 273.1596 | 299.3940 | 252.0294 | 315.7364 |
| Crepis biennis | 260.75 | 298.75 | 271.5887 | 298.9705 | 251.9801 | 307.3669 |
| Cruciata laevipes | 252.95 | 307.85 | 272.4844 | 300.2366 | 253.5713 | 308.2130 |
| Cynosurus cristatus | 259.65 | 301.75 | 272.5320 | 298.5170 | 254.8729 | 306.4642 |
| Dactylis glomerata | 256.65 | 305.15 | 272.1734 | 296.0881 | 252.0252 | 308.5776 |
| Daucus carota | 257.65 | 309.55 | 266.2151 | 296.4998 | 242.1676 | 310.9086 |
| Echium vulgare | 251.35 | 306.35 | 271.6662 | 298.8043 | 255.2059 | 319.5985 |
| Elytrigia repens | 254.15 | 295.55 | 271.7866 | 301.5160 | 247.5595 | 309.0756 |
| Epilobium angustifolium | 245.25 | 309.55 | 272.7190 | 297.2752 | 254.4250 | 309.6986 |
| Epilobium montanum | 251.35 | 298.55 | 271.8989 | 298.2934 | 255.0898 | 310.6581 |
| Equisetum arvense | 248.05 | 310.55 | 272.4469 | 298.5272 | 250.5626 | 307.0044 |
| Euphorbia cyparissias | 259.65 | 306.05 | 272.1290 | 297.1805 | 250.1373 | 310.3564 |
| Euphrasia officinalis | 249.55 | 298.55 | 270.7311 | 296.1804 | 253.3737 | 312.7560 |
| Falcaria vulgaris | 254.25 | 303.25 | 271.2448 | 298.7681 | 242.7748 | 312.5217 |
| Festuca ovina | 253.55 | 298.05 | 270.1712 | 298.0657 | 252.9897 | 318.4304 |
| Festuca rubra | 256.45 | 301.45 | 269.7952 | 298.5459 | 251.5868 | 306.3434 |
| Filipendula vulgaris | 250.95 | 302.05 | 272.8027 | 299.0400 | 252.4541 | 309.9816 |
| Fragaria vesca | 252.15 | 307.75 | 270.6255 | 298.6630 | 251.5961 | 310.7341 |
| Fragaria viridis | 246.45 | 302.65 | 270.9948 | 296.7431 | 256.0109 | 310.6857 |
| Galeopsis ladanum | 246.15 | 301.15 | 270.8524 | 299.1296 | 249.0760 | 321.3653 |
| Galium aparine | 262.45 | 311.45 | 273.2469 | 299.1077 | 253.5623 | 308.0681 |
| Galium mollugo | 246.45 | 303.65 | 270.4814 | 300.5872 | 250.4915 | 310.5497 |
| Galium pumilum | 261.35 | 301.95 | 270.8311 | 296.7854 | 251.5226 | 312.8000 |
| Galium verum | 246.65 | 304.15 | 270.8931 | 300.2993 | 254.5015 | 313.6896 |
| Genista tinctoria | 251.35 | 302.95 | 269.6055 | 296.7896 | 255.1989 | 309.4719 |
| Gentianella ciliata | 260.15 | 300.05 | 269.0697 | 298.7851 | 250.5263 | 310.6989 |
| Gentianella germanica | 259.55 | 298.25 | 273.7234 | 300.2318 | 250.9874 | 312.8136 |
| Geranium dissectum | 265.65 | 308.85 | 268.3877 | 298.3269 | 250.9249 | 315.9469 |
| Geranium molle | 263.85 | 309.75 | 270.3815 | 296.7747 | 247.0802 | 308.0363 |
| Geranium pratense | 246.65 | 299.55 | 272.3537 | 299.5207 | 255.4252 | 310.7380 |
| Geranium pusillum | 259.05 | 308.55 | 271.6294 | 297.4381 | 258.7324 | 309.6532 |
| Geranium robertianum | 258.35 | 304.15 | 272.4352 | 295.7082 | 257.0052 | 315.6884 |
| Geum urbanum | 251.35 | 303.75 | 273.4199 | 294.8184 | 241.3466 | 309.0217 |
| Gymnadenia conopsea | 249.35 | 301.45 | 270.4569 | 297.7843 | 248.1068 | 309.7297 |
| Helianthemum nummularium | 262.75 | 301.55 | 270.2152 | 299.2237 | 251.1202 | 310.6222 |
| Heracleum sphondylium | 250.15 | 306.05 | 272.0801 | 294.0913 | 254.7909 | 309.0031 |
| Hieracium caespitosum | 245.05 | 304.65 | 269.9496 | 296.2858 | 252.1705 | 310.4990 |
| Hieracium lachenalii | 256.35 | 301.65 | 273.2751 | 298.0204 | 247.0449 | 311.0437 |
| Hieracium murorum | 256.25 | 303.25 | 271.1487 | 296.6456 | 255.4890 | 307.9957 |
| Hieracium pilosella | 263.55 | 297.95 | 269.7212 | 296.8747 | 250.7250 | 312.0693 |
| Hippocrepis comosa | 262.85 | 302.65 | 271.4856 | 298.9020 | 249.4780 | 314.3369 |
| Holcus lanatus | 264.75 | 305.85 | 273.3224 | 294.6729 | 253.6496 | 307.9926 |
| Hypericum perforatum | 251.35 | 309.95 | 271.2771 | 294.8068 | 255.0486 | 308.5639 |
| Inula conyzae | 266.75 | 302.05 | 270.8331 | 300.2475 | 245.2510 | 307.6774 |
| Knautia arvensis | 255.95 | 308.15 | 272.0622 | 296.6081 | 253.0290 | 306.3252 |
| Koeleria pyramidata | 260.85 | 302.65 | 270.1596 | 296.9766 | 251.3631 | 309.5325 |
| Lathyrus pratensis | 249.55 | 301.05 | 270.4523 | 300.2842 | 251.0022 | 312.3451 |
| Leontodon hispidus | 256.55 | 304.45 | 270.9493 | 299.0820 | 254.5207 | 310.3506 |
| Leucanthemum vulgare | 257.65 | 308.75 | 269.5561 | 299.0105 | 256.4854 | 310.1190 |
| Linum catharticum | 259.95 | 298.95 | 273.6085 | 298.3147 | 246.6618 | 309.1260 |
| Listera ovata | 258.75 | 300.85 | 272.3620 | 299.4758 | 252.3227 | 308.2418 |
| Lolium perenne | 254.45 | 310.85 | 273.0840 | 297.7496 | 252.8932 | 305.0783 |
| Lotus corniculatus | 256.85 | 310.25 | 271.7461 | 298.7348 | 252.4370 | 309.6788 |
| Medicago falcata | 238.35 | 304.75 | 270.3372 | 294.6474 | 251.1171 | 305.4840 |
| Medicago lupulina | 256.95 | 309.65 | 271.7770 | 299.0080 | 252.1482 | 311.7032 |
| Melampyrum arvense | 253.45 | 303.65 | 271.2757 | 298.8676 | 256.2059 | 311.8046 |
| Melampyrum nemorosum | 248.55 | 300.35 | 270.3743 | 299.2537 | 256.9245 | 304.2698 |
| Melampyrum sylvaticum | 253.35 | 296.35 | 272.9939 | 298.3746 | 255.2842 | 308.5237 |
| Melilotus alba | 255.55 | 315.35 | 272.4317 | 298.3054 | 248.5222 | 308.8095 |
| Melilotus officinalis | 246.95 | 312.05 | 273.1676 | 296.4933 | 256.4789 | 311.4711 |
| Mentha arvensis | 242.55 | 304.95 | 272.3733 | 299.0519 | 242.5906 | 309.5487 |
| Mentha verticillata | 251.65 | 300.05 | 269.0971 | 298.0416 | 249.8646 | 306.8079 |
| Myosotis arvensis | 255.85 | 303.75 | 272.5385 | 296.7756 | 249.7126 | 308.5054 |
| Myosotis sylvatica | 250.45 | 302.85 | 273.2185 | 299.3862 | 251.8009 | 308.6125 |
| Onobrychis viciifolia | 246.65 | 306.35 | 271.4691 | 295.2230 | 255.0357 | 313.0150 |
| Ononis repens | 264.25 | 303.55 | 271.8451 | 296.1501 | 250.0200 | 317.0258 |
| Ononis spinosa | 252.45 | 304.75 | 272.6659 | 302.0783 | 251.6941 | 314.3262 |
| Ophrys apifera | 264.35 | 305.35 | 271.7948 | 297.7441 | 244.8758 | 309.4794 |
| Ophrys insectifera | 260.05 | 302.15 | 268.5181 | 295.6055 | 250.5786 | 312.6568 |
| Orchis mascula | 262.85 | 302.05 | 272.8969 | 296.6277 | 254.5777 | 310.8084 |
| Orchis militaris | 245.85 | 300.85 | 273.1736 | 297.9934 | 254.5207 | 305.4910 |
| Orchis purpurea | 263.15 | 303.25 | 273.0415 | 296.8596 | 256.3494 | 307.1148 |
| Orchis tridentata | 258.75 | 307.05 | 269.6941 | 301.4955 | 250.6753 | 314.4528 |
| Origanum vulgare | 249.55 | 308.25 | 274.0968 | 300.4442 | 251.2944 | 311.1488 |
| Phalaris arundinacea | 248.95 | 307.65 | 270.1711 | 294.7533 | 252.3157 | 319.0971 |
| Phleum pratense | 249.15 | 312.15 | 272.4265 | 299.5177 | 250.4977 | 316.5776 |
| Pimpinella saxifraga | 249.35 | 299.05 | 271.7754 | 295.8550 | 251.6258 | 312.9667 |
| Plantago lanceolata | 257.65 | 308.85 | 273.2015 | 297.2420 | 254.5082 | 308.9142 |
| Plantago major | 251.25 | 308.75 | 270.5416 | 300.7215 | 254.2079 | 316.0900 |
| Plantago media | 246.65 | 302.05 | 272.8899 | 297.4508 | 254.5479 | 313.7314 |
| Platanthera chlorantha | 259.35 | 301.45 | 271.7834 | 298.4653 | 257.3552 | 309.2955 |
| Poa annua | 252.15 | 308.85 | 269.5408 | 297.0792 | 250.3715 | 312.5413 |
| Poa pratensis | 249.55 | 307.65 | 272.6358 | 296.7814 | 253.0372 | 311.9918 |
| Poa trivialis | 250.95 | 303.35 | 271.9500 | 300.7099 | 247.9520 | 317.0152 |
| Polygala comosa | 255.85 | 300.65 | 272.5309 | 295.2580 | 249.4942 | 313.0809 |
| Polygala vulgaris | 258.95 | 302.15 | 271.5567 | 296.7614 | 249.9184 | 311.9826 |
| Potentilla anserina | 248.75 | 303.25 | 273.2915 | 298.9544 | 247.7343 | 312.2421 |
| Potentilla neumanniana | 264.45 | 301.95 | 270.7874 | 298.7110 | 251.6519 | 308.2401 |
| Potentilla reptans | 257.05 | 304.65 | 273.4994 | 301.9737 | 240.2226 | 307.6577 |
| Primula veris | 253.85 | 304.85 | 270.1969 | 296.7496 | 255.5011 | 310.6708 |
| Prunella grandiflora | 257.15 | 302.55 | 271.0463 | 298.9309 | 246.5766 | 313.9353 |
| Prunella vulgaris | 251.05 | 308.65 | 271.0987 | 300.5537 | 252.8913 | 316.0717 |
| Ranunculus bulbosus | 263.45 | 307.95 | 273.7090 | 297.4774 | 250.8227 | 316.0978 |
| Ranunculus repens | 257.85 | 308.75 | 272.4487 | 298.3711 | 249.2637 | 308.2412 |
| Rhinanthus angustifolius | 260.35 | 300.25 | 272.4395 | 295.6358 | 253.3451 | 316.7658 |
| Rhinanthus minor | 241.85 | 303.45 | 272.7668 | 295.9907 | 251.9525 | 308.3629 |
| Rumex acetosa | 256.95 | 304.75 | 273.4273 | 295.6055 | 251.7030 | 313.7984 |
| Rumex crispus | 250.55 | 313.75 | 272.3423 | 300.3409 | 256.8466 | 316.8586 |
| Salvia pratensis | 257.55 | 303.05 | 269.8330 | 301.4801 | 248.0749 | 315.8269 |
| Sanguisorba minor | 259.15 | 308.05 | 271.6971 | 301.0292 | 249.4543 | 308.0908 |
| Scabiosa columbaria | 262.35 | 303.15 | 271.3907 | 295.2953 | 254.7933 | 308.3580 |
| Sedum sexangulare | 255.45 | 304.75 | 271.9041 | 298.5656 | 250.6197 | 316.8384 |
| Senecio erucifolius | 254.45 | 302.05 | 272.7132 | 296.7972 | 257.5888 | 305.7738 |
| Senecio jacobaea | 265.35 | 300.05 | 271.9109 | 301.4701 | 254.9105 | 315.7778 |
| Senecio ovatus | 261.35 | 301.75 | 271.3432 | 303.4166 | 253.3430 | 308.8671 |
| Senecio vulgaris | 260.55 | 314.15 | 272.4001 | 296.2222 | 251.4537 | 314.7153 |
| Silene dioica | 259.65 | 303.95 | 271.5408 | 295.4608 | 251.6519 | 310.6632 |
| Silene nutans | 247.45 | 301.05 | 273.9750 | 296.0419 | 253.3505 | 310.7741 |
| Sonchus asper | 253.75 | 314.45 | 272.5625 | 297.9331 | 246.7858 | 311.7656 |
| Stachys germanica | 264.35 | 306.25 | 271.9508 | 299.3879 | 253.4679 | 312.1167 |
| Stellaria holostea | 258.85 | 301.35 | 270.4487 | 298.8171 | 250.5873 | 310.2129 |
| Stellaria media | 263.25 | 309.95 | 271.9365 | 299.5320 | 249.6857 | 317.1198 |
| Symphytum officinale | 256.25 | 304.55 | 272.8940 | 293.8011 | 249.6729 | 315.5533 |
| Tanacetum vulgare | 250.95 | 303.75 | 271.3712 | 296.8634 | 255.1434 | 312.0101 |
| Taraxacum laevigatum | 254.65 | 305.85 | 274.3273 | 297.0333 | 258.1710 | 307.2489 |
| Taraxacum officinale | 263.45 | 314.15 | 271.0948 | 302.3491 | 250.2633 | 308.9863 |
| Thymus pulegioides | 250.55 | 303.65 | 267.8125 | 299.4641 | 256.6491 | 319.3788 |
| Tragopogon pratensis | 254.95 | 308.65 | 271.6778 | 302.8890 | 253.1620 | 320.9419 |
| Trifolium campestre | 261.35 | 309.55 | 267.3581 | 298.5401 | 252.4395 | 306.2908 |
| Trifolium dubium | 263.45 | 308.25 | 270.9856 | 301.3185 | 252.8330 | 310.8504 |
| Trifolium medium | 251.75 | 302.25 | 272.4403 | 296.5950 | 249.7976 | 306.6114 |
| Trifolium ochroleucon | 264.35 | 305.75 | 274.2243 | 296.9874 | 256.5960 | 308.7642 |
| Trifolium pratense | 253.65 | 307.25 | 273.3055 | 299.0891 | 251.9699 | 313.8398 |
| Trifolium repens | 261.15 | 309.55 | 273.2943 | 297.3369 | 256.9904 | 313.4223 |
| Trisetum flavescens | 260.15 | 303.95 | 273.3681 | 299.0189 | 254.5060 | 309.9008 |
| Triticum aestivum | 251.45 | 315.45 | 270.6903 | 295.8165 | 244.3551 | 316.7203 |
| Valeriana dioica | 254.25 | 297.85 | 274.3191 | 296.2704 | 248.7697 | 310.0067 |
| Valeriana officinalis | 249.05 | 305.15 | 273.5390 | 294.0520 | 251.8219 | 311.3888 |
| Verbascum lychnitis | 246.75 | 302.95 | 271.0822 | 297.4175 | 253.9272 | 314.5745 |
| Veronica chamaedrys | 251.05 | 302.35 | 270.3170 | 296.6113 | 255.1450 | 310.5025 |
| Veronica teucrium | 249.95 | 302.15 | 273.1595 | 298.4734 | 257.5285 | 311.1516 |
| Vicia cracca | 246.75 | 307.25 | 272.8860 | 299.2021 | 250.5196 | 307.2705 |
| Vicia hirsuta | 251.65 | 306.65 | 269.8698 | 296.2084 | 253.0681 | 315.5485 |
| Vicia sepium | 251.05 | 298.55 | 272.3013 | 296.3016 | 251.3703 | 320.3166 |
| Vicia tenuifolia | 251.15 | 303.75 | 270.3606 | 297.5313 | 251.0760 | 309.5666 |
| Vicia tetrasperma | 256.55 | 308.15 | 271.9840 | 296.4055 | 253.0773 | 317.6524 |
| Vicia villosa | 269.05 | 309.75 | 273.2232 | 297.3986 | 253.1552 | 315.2229 |
| Vincetoxicum hirundinaria | 251.65 | 304.55 | 271.9334 | 304.0549 | 247.2298 | 309.1991 |
| Viola hirta | 250.65 | 300.75 | 273.1739 | 299.2233 | 241.8610 | 309.0994 |
| Viola odorata | 260.65 | 307.45 | 271.7810 | 296.9289 | 248.8729 | 308.2617 |
9 Appendix 4: List of species and trait values (Table @ref(tab:edm-traits))
## compile traits processed in Data evaluation
spptraits <- traitsleda_complete_unif%>%
ungroup()%>%
rename(species = sp)%>%
left_join(., pollination)%>%
mutate(kernel = case_when(kernel == "any" ~ "long-medium-short",
TRUE ~ kernel))%>%
## reclassify seed mass and maximal mass after imputation
mutate(seedmass = case_when(
seedmass <= 0.0001 ~ 0.0001,
seedmass > 0.0001 & seedmass <= 0.0003 ~ 0.0003,
seedmass > 0.0003 ~ 0.001))%>%
mutate(compartsize = case_when(
seedmass == 0.0001 ~ 10,
seedmass == 0.0003 ~ 20,
seedmass == 0.001 ~ 50))%>%
left_join(., dplyr::select(growth_pars, seedmass, b0grow), by="seedmass")%>%
mutate(b0germ = b0germ)%>%
mutate(b0mort = b0mort)%>%
# Reproductive traits
left_join(., flowerphenology_edm)%>%
mutate(seedon = floroff - 1)%>%
mutate(seedoff = seedon + 4)%>%
mutate(seednumber_min = seednumber_min/(seedoff-seedon),
seednumber_max = seednumber_max/(seedoff-seedon))%>%
inner_join(., bioclim_edm.df)%>%
#order columns
dplyr::select(species, kernel, clonality, pollen_vector,
self_failoutcross = selfing_at_failoutcross,
seedmass, compartsize, self_proba, span_min, span_max, firstflower_min,
firstflower_max, bankduration_max, bankduration_min, seednumber_min,
seednumber_max, b0grow, b0germ, b0mort, floron, floroff,
seedon, seedoff,
temp_opt, temp_tol)
## Festuca pratensis is missing from the gbif
spptraits[which(species == "Festuca pratensis"), "temp_opt"] <- spptraits[which(species == "Festuca rubra"), "temp_opt"]
spptraits[which(species == "Festuca pratensis"), "temp_tol"] <- spptraits[which(species == "Festuca rubra"), "temp_tol"]
write.csv(spptraits, file.path(EDMdocsdir, "spp_traits" ,"EDM_spptraits.csv"),
row.names = FALSE)
write.csv(spptraits, file.path(EDMdir, "template_files" ,"EDM_spptraits.csv"),
row.names = FALSE)edm_traits %>%
mk_itspp() %>%
rename("Species" = "species",
"Kernel" = "kernel",
"Clonality" = "clonality",
"Pollen vector" = "pollen_vector",
"Selfing" = "self_failoutcross",
"Seed biomass" = "seedmass",
"Allocation size" = "compartsize",
"Prob. selfing" = "self_proba",
"Min. life span" = "span_min",
"Max. life span" = "span_max",
"Min. age 1st flower" = "firstflower_min",
"Max. age 1st flower" = "firstflower_max",
"Max. seed bank" = "bankduration_max",
"Min. seed bank" = "bankduration_min",
"Min. seeds" = "seednumber_min",
"Max. seeds" = "seednumber_max",
"Norm. constant growth" = "b0grow",
"Norm. constant germ." = "b0germ",
"Norm. constant mort" = "b0mort",
"Begin flowering" = "floron",
"End flowering" = "floroff",
"Begin sowing" = "seedon",
"End sowing" = "seedoff",
"Temp. optimum" = "temp_opt",
"Temp. tolerance" = "temp_tol") %>%
knitr::kable(caption = "Final trait values defined for the species input used in the first implementation of the model, Figueiredo et al. (in prep.)",
longtable = TRUE, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "700px")| Species | Kernel | Clonality | Pollen vector | Selfing | Seed biomass | Allocation size | Prob. selfing | Min. life span | Max. life span | Min. age 1st flower | Max. age 1st flower | Max. seed bank | Min. seed bank | Min. seeds | Max. seeds | Norm. constant growth | Norm. constant germ. | Norm. constant mort | Begin flowering | End flowering | Begin sowing | End sowing | Temp. optimum | Temp. tolerance |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Achillea millefolium | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 109.590564 | 7.110712 | 47.5000 | 1800.000 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 285.6251 | 9.566667 |
| Acinos arvensis | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 385.780391 | 3.875541 | 85.5000 | 85.500 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 278.8466 | 7.666667 |
| Actaea spicata | long | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 27.0000 | 475.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 278.4126 | 7.933333 |
| Aegopodium podagraria | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 385.780391 | 3.875541 | 11.6250 | 2675.000 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.6411 | 9.116667 |
| Agrimonia eupatoria | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.950 | 1560 | 1560 | 105.595880 | 105.595880 | 149.458251 | 3.875541 | 16.5000 | 16.500 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 280.4205 | 8.950000 |
| Ajuga genevensis | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 385.780391 | 3.875541 | 55.2500 | 55.250 | 8058178821 | 141363714 | 1113239249 | 14 | 26 | 25 | 29 | 281.5398 | 8.000000 |
| Ajuga reptans | short-medium | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 2600 | 105.595880 | 105.595880 | 122.684719 | 7.110712 | 9.0000 | 26.250 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 283.3404 | 8.083333 |
| Allium schoenoprasum | short-long | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 140.741479 | 7.110712 | 1037.2500 | 1037.250 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.3545 | 9.183333 |
| Alopecurus pratensis | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.626250 | 7.110712 | 103.5000 | 103.500 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.7221 | 8.766667 |
| Anchusa arvensis | long-medium | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 149.458251 | 3.875541 | 32.0000 | 175.000 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 281.2234 | 9.566667 |
| Antennaria dioica | long-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 2600 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 70.0000 | 70.000 | 4531446957 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 278.2855 | 8.700000 |
| Anthoxanthum odoratum | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.825074 | 7.110712 | 27.0000 | 259.625 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 281.2262 | 7.800000 |
| Anthriscus sylvestris | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.226599 | 7.110712 | 33.0250 | 2500.000 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 281.5486 | 7.483333 |
| Anthyllis vulneraria | long | TRUE | insects | FALSE | 0.0010 | 50 | 0.500 | 52 | 1560 | 26.000000 | 26.000000 | 385.780391 | 3.875541 | 7.5000 | 48.500 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 280.7263 | 8.266667 |
| Arabis hirsuta | long-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.500 | 1560 | 1560 | 105.595880 | 105.595880 | 81.543282 | 7.110712 | 187.5000 | 187.500 | 4531446957 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.1182 | 8.333333 |
| Arrhenatherum elatius | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 170.580602 | 7.110712 | 2.5000 | 2.500 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 281.2127 | 7.400000 |
| Artemisia vulgaris | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 39.577009 | 7.110712 | 50000.0000 | 125000.000 | 5963719581 | 141363714 | 1113239249 | 27 | 49 | 48 | 52 | 283.0275 | 9.566667 |
| Astragalus glycyphyllos | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 139.019233 | 7.110712 | 2578.7500 | 2578.750 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 279.8322 | 8.416667 |
| Avenula pubescens | short-long | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 140.783097 | 7.110712 | 1000.0000 | 1000.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 279.8673 | 7.916667 |
| Brachypodium pinnatum | short-long | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 78.283972 | 102.511805 | 146.757600 | 7.110712 | 21.7500 | 21.750 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.0725 | 9.000000 |
| Bellis perennis | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.500 | 1560 | 1560 | 105.595880 | 105.595880 | 95.782183 | 7.110712 | 322.0000 | 322.000 | 5963719581 | 141363714 | 1113239249 | 0 | 49 | 48 | 52 | 283.4846 | 7.416667 |
| Briza media | short-long | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.346279 | 7.110712 | 44.7500 | 1827.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 279.9232 | 8.733333 |
| Bromus erectus | long | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 281.7318 | 6.133333 |
| Bromus hordeaceus | long | TRUE | wind | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 218.104238 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 282.5224 | 8.016667 |
| Calystegia sepium | long-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 385.780391 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 282.3955 | 9.350000 |
| Campanula glomerata | short-long | TRUE | insects | TRUE | 0.0003 | 20 | 0.000 | 52 | 1560 | 26.000000 | 26.000000 | 149.458251 | 3.875541 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 277.7940 | 11.166667 |
| Campanula persicifolia | long-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 104 | 1560 | 18.772773 | 18.772773 | 115.372483 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 279.8406 | 7.600000 |
| Campanula rotundifolia | long-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 94.852415 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 279.3996 | 9.083333 |
| Campanula rapunculoides | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 98.990475 | 7.110712 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 279.5601 | 9.683333 |
| Cardaminopsis arenosa | long | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 149.458251 | 149.458251 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 14 | 35 | 34 | 38 | 280.3846 | 7.900000 |
| Carduus crispus | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 104 | 104 | 52.000000 | 52.000000 | 385.780391 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 279.9315 | 8.850000 |
| Carex caryophyllea | medium | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 14 | 22 | 21 | 25 | 280.5223 | 8.466667 |
| Carex flacca | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 156.145247 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.1753 | 7.533333 |
| Carex leporina | short | FALSE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 279.1100 | 7.100000 |
| Carex ornithopoda | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 22 | 21 | 25 | 278.5075 | 7.466667 |
| Carlina vulgaris | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 173.485193 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.7739 | 6.816667 |
| Centaurea jacea | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 49 | 48 | 52 | 280.4447 | 8.633333 |
| Centaurea scabiosa | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 156.145247 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 279.9491 | 9.300000 |
| Centaurium erythraea | short-long | FALSE | insects | FALSE | 0.0001 | 10 | 0.950 | 52 | 1560 | 17.398469 | 47.747222 | 111.678497 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 284.0660 | 9.666667 |
| Cephalanthera rubra | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.500 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.1061 | 8.700000 |
| Cerastium arvense | long-short | TRUE | insects | TRUE | 0.0003 | 20 | 0.000 | 1560 | 2600 | 18.772773 | 105.595880 | 79.795809 | 7.110712 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 14 | 31 | 30 | 34 | 281.0167 | 9.916667 |
| Cerastium holosteoides | short | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 9 | 26 | 25 | 29 | 281.2091 | 7.183333 |
| Cerastium tomentosum | long-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 169.978778 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.0699 | 7.233333 |
| Cerinthe minor | short-long | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 104 | 1560 | 18.772773 | 18.772773 | 172.665941 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.6468 | 7.683333 |
| Chaerophyllum hirsutum | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.2983 | 6.316667 |
| Chaerophyllum temulum | short-long | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 104 | 6.331742 | 9.276826 | 149.458251 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 282.3427 | 6.933333 |
| Cichorium intybus | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 149.458251 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 44 | 43 | 47 | 283.5332 | 9.600000 |
| Cirsium acaule | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.5920 | 8.450000 |
| Cirsium arvense | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 281.8333 | 9.083333 |
| Cirsium oleraceum | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 169.978778 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 280.1936 | 8.366667 |
| Cirsium tuberosum | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 282.1654 | 6.016667 |
| Cirsium vulgare | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 191.231706 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 283.7613 | 8.616667 |
| Clinopodium vulgare | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.3037 | 9.183333 |
| Convolvulus arvensis | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.500 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 284.1778 | 10.683333 |
| Crepis biennis | long-medium-short | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 104 | 104 | 18.772773 | 18.772773 | 217.371379 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 281.6483 | 6.333333 |
| Cruciata laevipes | long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 14 | 26 | 25 | 29 | 282.3399 | 9.150000 |
| Cynosurus cristatus | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.500 | 1560 | 1560 | 18.772773 | 105.595880 | 156.145247 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.0622 | 7.016667 |
| Dactylis glomerata | long-short | TRUE | wind | TRUE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 105.595880 | 156.145247 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 282.0465 | 8.083333 |
| Daucus carota | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 105.595880 | 156.145247 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 285.5320 | 8.650000 |
| Echium vulgare | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 104 | 104 | 18.772773 | 18.772773 | 385.780391 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 282.0942 | 9.166667 |
| Elytrigia repens | long-medium-short | FALSE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 279.0669 | 6.900000 |
| Epilobium angustifolium | long-medium-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 94.852415 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 279.1531 | 10.716667 |
| Epilobium montanum | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.950 | 1560 | 1560 | 18.772773 | 18.772773 | 112.824005 | 7.110712 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 279.5080 | 7.866667 |
| Equisetum arvense | long-medium-short | FALSE | wind | FALSE | 0.0001 | 10 | 0.500 | 1560 | 1560 | 63.036330 | 105.045780 | 74.979452 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 9 | 17 | 16 | 20 | 281.6585 | 10.416667 |
| Euphorbia cyparissias | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 14 | 22 | 21 | 25 | 282.5568 | 7.733333 |
| Euphrasia officinalis | long-medium-short | FALSE | insects | TRUE | 0.0003 | 20 | 0.000 | 52 | 52 | 18.772773 | 18.772773 | 149.458251 | 3.875541 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 279.2117 | 8.166667 |
| Falcaria vulgaris | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 26.000000 | 26.000000 | 3.875541 | 3.875541 | 500.0000 | 500.000 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 281.5397 | 8.166667 |
| Festuca ovina | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 104 | 1560 | 52.000000 | 52.000000 | 181.360109 | 7.110712 | 211.7500 | 211.750 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 279.3919 | 7.416667 |
| Festuca pratensis | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 279.8448 | 7.500000 |
| Festuca rubra | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 104 | 1560 | 39.427771 | 52.331844 | 183.414557 | 7.110712 | 116.2500 | 116.250 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 279.8448 | 7.500000 |
| Filipendula vulgaris | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 28.2500 | 109.825 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.5264 | 8.516667 |
| Fragaria vesca | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.831881 | 7.110712 | 56.2500 | 85.250 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 282.1414 | 9.266667 |
| Fragaria viridis | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 310.486899 | 310.486899 | 83.123572 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 278.8633 | 9.366667 |
| Galeopsis ladanum | long-medium | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 52 | 18.772773 | 18.772773 | 221.118505 | 7.110712 | 75.0000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 279.7941 | 9.166667 |
| Galium aparine | long | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 220.186553 | 7.110712 | 7.7500 | 250.000 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 287.8325 | 8.166667 |
| Galium mollugo | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 169.213543 | 7.110712 | 433.5000 | 5000.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.4929 | 9.533333 |
| Galium pumilum | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 281.4022 | 6.766667 |
| Galium verum | short-long | TRUE | wind-insects | FALSE | 0.0010 | 50 | 0.025 | 1560 | 1560 | 105.595880 | 105.595880 | 141.892395 | 7.110712 | 3.1500 | 26.025 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 280.0749 | 9.583333 |
| Genista tinctoria | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 89.879767 | 119.343549 | 3.875541 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.7595 | 8.600000 |
| Gentianella ciliata | short | TRUE | insects | FALSE | 0.0003 | 20 | 0.050 | 104 | 1560 | 52.000000 | 52.000000 | 3.875541 | 3.875541 | 167.5000 | 167.500 | 5963719581 | 141363714 | 1113239249 | 32 | 44 | 43 | 47 | 280.3280 | 6.650000 |
| Gentianella germanica | short | FALSE | insects | TRUE | 0.0003 | 20 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 174.418046 | 7.110712 | 40.0000 | 148.500 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 280.6513 | 6.450000 |
| Geranium dissectum | long-short | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 220.171780 | 7.110712 | 23.7500 | 250.000 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 285.2910 | 7.200000 |
| Geranium molle | long-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 385.780391 | 3.875541 | 20.2500 | 25.000 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 284.3925 | 7.650000 |
| Geranium pratense | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.899512 | 7.110712 | 0.7500 | 0.750 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 279.9600 | 8.816667 |
| Geranium pusillum | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 1560 | 18.772773 | 18.772773 | 193.253455 | 7.110712 | 75.0000 | 187.500 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 280.8284 | 8.250000 |
| Geranium robertianum | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 220.220719 | 7.110712 | 7.5000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 282.1439 | 7.633333 |
| Geum urbanum | long | TRUE | insects | FALSE | 0.0010 | 50 | 0.950 | 1560 | 1560 | 18.772773 | 105.595880 | 155.686450 | 7.110712 | 43.0000 | 81.125 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 281.6276 | 8.733333 |
| Gymnadenia conopsea | short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 7924.7500 | 7924.750 | 4531446957 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 278.6253 | 8.683333 |
| Helianthemum nummularium | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.832477 | 7.110712 | 60.7500 | 60.750 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 280.1858 | 6.466667 |
| Heracleum sphondylium | long-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.219106 | 7.110712 | 212.5000 | 2501.250 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 283.5358 | 9.316667 |
| Hieracium caespitosum | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 153.0000 | 153.000 | 5963719581 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 281.5453 | 9.933333 |
| Hieracium lachenalii | short | FALSE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.7973 | 7.550000 |
| Hieracium murorum | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 280.0843 | 7.833333 |
| Hieracium pilosella | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.050 | 1560 | 1560 | 18.772773 | 105.595880 | 109.974990 | 7.110712 | 5.2500 | 18.750 | 5963719581 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 282.3518 | 5.733333 |
| Hippocrepis comosa | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.866833 | 7.110712 | 30.0000 | 30.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.8446 | 6.633333 |
| Holcus lanatus | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.500 | 1560 | 1560 | 18.772773 | 105.595880 | 106.303418 | 7.110712 | 44250.0000 | 44250.000 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 282.6703 | 6.850000 |
| Hypericum perforatum | long-medium-short | TRUE | insects | FALSE | 0.0001 | 10 | 0.050 | 1560 | 1560 | 105.595880 | 105.595880 | 79.452138 | 7.110712 | 759.0000 | 8250.000 | 4531446957 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 282.1337 | 9.766667 |
| Inula conyzae | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 282.6778 | 5.883333 |
| Knautia arvensis | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 310.486899 | 106.764162 | 7.110712 | 16.5000 | 500.000 | 8058178821 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 280.4889 | 8.700000 |
| Koeleria pyramidata | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 78.242321 | 102.473579 | 146.736558 | 7.110712 | 52.3750 | 52.375 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.7134 | 6.966667 |
| Lathyrus pratensis | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.718570 | 7.110712 | 9.5000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 279.4419 | 8.583333 |
| Leontodon hispidus | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.723208 | 7.110712 | 16.5000 | 17.750 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 280.8981 | 7.983333 |
| Leucanthemum vulgare | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 169.307675 | 7.110712 | 16.5250 | 6500.000 | 8058178821 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 282.6034 | 8.516667 |
| Linum catharticum | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.950 | 52 | 1560 | 18.772773 | 18.772773 | 147.589150 | 7.110712 | 2.8500 | 45.750 | 5963719581 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.3002 | 6.500000 |
| Listera ovata | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 105.595880 | 310.486899 | 3.875541 | 3.875541 | 5250.0000 | 37500.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.2161 | 7.016667 |
| Lolium perenne | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 191.231706 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 286.1423 | 9.400000 |
| Lotus corniculatus | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 154.789620 | 7.110712 | 86.2500 | 4500.000 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 281.5991 | 8.900000 |
| Medicago falcata | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 278.8608 | 11.066667 |
| Medicago lupulina | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.500 | 52 | 1560 | 18.772773 | 18.772773 | 193.016018 | 7.110712 | 25.0000 | 1650.000 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 287.8328 | 8.783333 |
| Melampyrum arvense | long-medium | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 149.458251 | 3.875541 | 2.5000 | 10.000 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 281.3895 | 8.366667 |
| Melampyrum nemorosum | medium | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 52 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 3.0000 | 109.500 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 279.1104 | 8.633333 |
| Melampyrum sylvaticum | medium-long | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 52 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 2.3500 | 19.000 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 278.0563 | 7.166667 |
| Melilotus alba | long-short | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 104 | 26.000000 | 26.000000 | 199.897132 | 7.110712 | 1071.8750 | 87500.000 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 283.4032 | 9.966667 |
| Melilotus officinalis | long-medium-short | FALSE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 215.371175 | 7.110712 | 25.0000 | 25000.000 | 8058178821 | 141363714 | 1113239249 | 14 | 40 | 39 | 43 | 282.2428 | 10.850000 |
| Mentha arvensis | long | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 96.127741 | 7.110712 | 4.8750 | 50.000 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 278.4783 | 10.400000 |
| Mentha verticillata | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 27 | 35 | 34 | 38 | 280.1657 | 8.066667 |
| Myosotis arvensis | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 220.058900 | 7.110712 | 125.0000 | 350.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.5512 | 7.983333 |
| Myosotis sylvatica | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 1560 | 1560 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 48.0000 | 48.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.8248 | 8.733333 |
| Onobrychis viciifolia | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 4.0000 | 4.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.8257 | 9.950000 |
| Ononis repens | short-medium | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 170.582970 | 7.110712 | 0.2500 | 1.000 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 282.5768 | 6.550000 |
| Ononis spinosa | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 75.0000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.1944 | 8.716667 |
| Ophrys apifera | short | TRUE | insects | FALSE | 0.0010 | 50 | 1.000 | 1560 | 1560 | 105.595880 | 310.486899 | 61.763858 | 7.110712 | 10000.0000 | 10000.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 283.7225 | 6.833333 |
| Ophrys insectifera | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 281.0236 | 7.016667 |
| Orchis mascula | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 310.486899 | 310.486899 | 79.624843 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 281.6205 | 6.533333 |
| Orchis militaris | short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 105.595880 | 310.486899 | 36.356334 | 7.110712 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 281.0057 | 9.166667 |
| Orchis purpurea | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 310.486899 | 310.486899 | 52.673546 | 7.110712 | 1500.0000 | 1500.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 283.7867 | 6.683333 |
| Orchis tridentata | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 288.6735 | 8.050000 |
| Origanum vulgare | short-long | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 95.593185 | 7.110712 | 1.0000 | 1.000 | 4531446957 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.4336 | 9.783333 |
| Phalaris arundinacea | long-medium-short | TRUE | wind | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.062410 | 7.110712 | 750.0000 | 750.000 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.5887 | 9.783333 |
| Phleum pratense | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.025 | 1560 | 1560 | 78.083368 | 102.327697 | 146.656256 | 7.110712 | 169.2500 | 169.250 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.0307 | 10.500000 |
| Pimpinella saxifraga | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.374734 | 7.110712 | 67.0000 | 2391.500 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 279.1678 | 8.283333 |
| Plantago lanceolata | long-medium-short | TRUE | wind | TRUE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 192.739234 | 7.110712 | 146.2500 | 2500.000 | 8058178821 | 141363714 | 1113239249 | 18 | 44 | 43 | 47 | 284.8469 | 8.533333 |
| Plantago major | long-medium-short | TRUE | wind | TRUE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 107.913881 | 7.110712 | 141.2500 | 10000.000 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 283.9908 | 9.583333 |
| Plantago media | long-short | TRUE | wind-insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.316104 | 7.110712 | 70.5000 | 1860.000 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 279.5996 | 9.233333 |
| Platanthera chlorantha | short | TRUE | insects | FALSE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 310.486899 | 310.486899 | 3.875541 | 3.875541 | 6514.3144 | 21841.484 | 4531446957 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.1140 | 7.016667 |
| Poa annua | long-medium-short | TRUE | wind | TRUE | 0.0003 | 20 | 0.050 | 1560 | 1560 | 18.772773 | 18.772773 | 124.171669 | 7.110712 | 25.0000 | 3250.000 | 5963719581 | 141363714 | 1113239249 | 0 | 53 | 52 | 56 | 284.4375 | 9.450000 |
| Poa pratensis | long-short | TRUE | wind | TRUE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 109.925377 | 7.110712 | 52.0000 | 52.000 | 5963719581 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.0435 | 9.683333 |
| Poa trivialis | long-medium-short | TRUE | wind | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 82.981780 | 120.228460 | 97.161759 | 7.110712 | 74.8750 | 74.875 | 5963719581 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.7038 | 8.733333 |
| Polygala comosa | medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 54.296164 | 69.486893 | 385.780391 | 3.875541 | 4.5000 | 4.500 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.1609 | 7.466667 |
| Polygala vulgaris | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.689375 | 7.110712 | 47.0000 | 47.000 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 279.9018 | 7.200000 |
| Potentilla anserina | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.890839 | 7.110712 | 2.4250 | 37.500 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 281.3451 | 9.083333 |
| Potentilla neumanniana | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 14 | 26 | 25 | 29 | 283.0755 | 6.250000 |
| Potentilla reptans | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 112.824005 | 7.110712 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 281.6585 | 7.933333 |
| Primula veris | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.831812 | 7.110712 | 60.0000 | 67.750 | 8058178821 | 141363714 | 1113239249 | 14 | 26 | 25 | 29 | 281.5454 | 8.500000 |
| Prunella grandiflora | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 78.293832 | 102.520854 | 146.762582 | 7.110712 | 14.5000 | 14.500 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.7381 | 7.566667 |
| Prunella vulgaris | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 193.307668 | 7.110712 | 10.8250 | 213.500 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 285.1315 | 9.600000 |
| Ranunculus bulbosus | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.730099 | 7.110712 | 5.7500 | 33.400 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.8991 | 7.416667 |
| Ranunculus repens | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 170.568341 | 7.110712 | 5.0000 | 53.825 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 282.5288 | 8.483333 |
| Rhinanthus angustifolius | long-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 52 | 18.772773 | 18.772773 | 221.128254 | 7.110712 | 15.8750 | 306.250 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 281.7180 | 6.650000 |
| Rhinanthus minor | long-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.100 | 52 | 52 | 18.772773 | 18.772773 | 221.188756 | 7.110712 | 4.8500 | 46.750 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 279.8482 | 10.266667 |
| Rumex acetosa | long-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.788064 | 7.110712 | 11.3500 | 525.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.8967 | 7.966667 |
| Rumex crispus | long-medium-short | TRUE | wind | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 191.177517 | 7.110712 | 262.5000 | 10000.000 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 287.1484 | 10.533333 |
| Salvia pratensis | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 105.595880 | 155.033887 | 7.110712 | 525.0000 | 1151.250 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 282.3332 | 7.583333 |
| Sanguisorba minor | long-short | TRUE | wind | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.741048 | 7.110712 | 0.7500 | 0.750 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 282.4030 | 8.150000 |
| Scabiosa columbaria | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 104 | 1560 | 18.772773 | 18.772773 | 192.553080 | 7.110712 | 6.0000 | 77.750 | 8058178821 | 141363714 | 1113239249 | 27 | 49 | 48 | 52 | 281.7792 | 6.800000 |
| Sedum sexangulare | long-medium-short | TRUE | insects | TRUE | 0.0001 | 10 | 0.000 | 1560 | 1560 | 62.993035 | 93.284943 | 385.780391 | 3.875541 | 37.0000 | 37.000 | 4531446957 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 281.2399 | 8.216667 |
| Senecio erucifolius | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 385.780391 | 3.875541 | 287.8750 | 518.750 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 281.9572 | 7.933333 |
| Senecio jacobaea | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 1560 | 18.772773 | 18.772773 | 188.026383 | 7.110712 | 525.0000 | 25000.000 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 282.1799 | 5.783333 |
| Senecio ovatus | short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.0713 | 6.733333 |
| Senecio vulgaris | long-medium-short | FALSE | insects | FALSE | 0.0003 | 20 | 0.950 | 52 | 1560 | 18.772773 | 18.772773 | 145.592239 | 7.110712 | 180.0000 | 9500.000 | 5963719581 | 141363714 | 1113239249 | 5 | 49 | 48 | 52 | 285.8002 | 8.933333 |
| Silene dioica | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 104 | 1560 | 52.000000 | 52.000000 | 181.095047 | 7.110712 | 55.0000 | 2325.000 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 282.0698 | 7.383333 |
| Silene nutans | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 18.772773 | 105.595880 | 155.670437 | 7.110712 | 63.9500 | 63.950 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 279.9228 | 8.933333 |
| Sonchus asper | long-medium-short | FALSE | insects | FALSE | 0.0003 | 20 | 0.500 | 52 | 1560 | 18.772773 | 18.772773 | 118.436898 | 7.110712 | 82.5000 | 150000.000 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 289.9740 | 10.116667 |
| Stachys germanica | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 144.127994 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 284.6420 | 6.983333 |
| Stellaria holostea | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.895043 | 7.110712 | 4.7500 | 4.750 | 8058178821 | 141363714 | 1113239249 | 14 | 22 | 21 | 25 | 282.0927 | 7.083333 |
| Stellaria media | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.500 | 52 | 1560 | 18.772773 | 18.772773 | 190.092700 | 7.110712 | 2750.0000 | 3750.000 | 8058178821 | 141363714 | 1113239249 | 0 | 53 | 52 | 56 | 285.8061 | 7.783333 |
| Symphytum officinale | medium-long | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 3.875541 | 3.875541 | 54.0000 | 350.000 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 282.1790 | 8.050000 |
| Tanacetum vulgare | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.196255 | 7.110712 | 106.2500 | 3125.000 | 8058178821 | 141363714 | 1113239249 | 27 | 40 | 39 | 43 | 280.7742 | 8.800000 |
| Taraxacum laevigatum | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 14 | 26 | 25 | 29 | 284.2226 | 8.533333 |
| Taraxacum officinale | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 76.966511 | 105.261698 | 130.743676 | 7.110712 | 1472.4605 | 5739.778 | 8058178821 | 141363714 | 1113239249 | 9 | 44 | 43 | 47 | 287.2466 | 8.450000 |
| Thymus pulegioides | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 95.994321 | 7.110712 | 132.1250 | 132.125 | 5963719581 | 141363714 | 1113239249 | 23 | 44 | 43 | 47 | 280.7396 | 8.850000 |
| Tragopogon pratensis | long-medium-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 104 | 1560 | 18.772773 | 18.772773 | 149.458251 | 3.875541 | 47.5000 | 47.500 | 8058178821 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.7897 | 8.950000 |
| Trifolium campestre | long-short | TRUE | insects | TRUE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 220.202386 | 7.110712 | 35.6250 | 35.625 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 284.8373 | 8.033333 |
| Trifolium dubium | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.500 | 52 | 52 | 18.772773 | 18.772773 | 221.176603 | 7.110712 | 23.0000 | 23.000 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 284.5877 | 7.466667 |
| Trifolium medium | long-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 89.181933 | 118.634286 | 3.875541 | 3.875541 | 7.5000 | 27.500 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 279.1436 | 8.416667 |
| Trifolium ochroleucon | long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 78.220050 | 102.453140 | 146.725307 | 7.110712 | 68.7500 | 68.750 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 283.1174 | 6.900000 |
| Trifolium pratense | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 105.595880 | 155.593849 | 7.110712 | 132.5000 | 132.500 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 284.0001 | 8.933333 |
| Trifolium repens | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 2600 | 18.772773 | 18.772773 | 174.093581 | 7.110712 | 16.0000 | 250.000 | 8058178821 | 141363714 | 1113239249 | 18 | 40 | 39 | 43 | 289.0059 | 8.066667 |
| Trisetum flavescens | long-short | TRUE | wind | FALSE | 0.0003 | 20 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 117.842149 | 7.110712 | 6250.0000 | 6250.000 | 5963719581 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 281.7958 | 7.300000 |
| Triticum aestivum | long | TRUE | wind | FALSE | 0.0010 | 50 | 0.050 | 52 | 52 | 18.772773 | 18.772773 | 221.070603 | 7.110712 | 117.8750 | 117.875 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.4158 | 10.666667 |
| Valeriana dioica | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 78.300865 | 102.526838 | 146.767958 | 7.110712 | 6.2500 | 15.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.8733 | 7.266667 |
| Valeriana officinalis | long | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 18.772773 | 18.772773 | 3.875541 | 3.875541 | 34.7500 | 164.250 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 279.3240 | 9.350000 |
| Verbascum lychnitis | long-short | FALSE | insects | TRUE | 0.0003 | 20 | 0.000 | 104 | 104 | 52.000000 | 52.000000 | 385.780391 | 3.875541 | 22052.7500 | 32862.375 | 5963719581 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.6361 | 9.366667 |
| Veronica chamaedrys | long-medium-short | TRUE | insects | FALSE | 0.0003 | 20 | 0.050 | 1560 | 1560 | 105.595880 | 105.595880 | 96.119430 | 7.110712 | 0.7500 | 112.500 | 5963719581 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 281.1476 | 8.550000 |
| Veronica teucrium | long | FALSE | insects | FALSE | 0.0003 | 20 | 0.500 | 1560 | 1560 | 67.366755 | 105.105784 | 92.342268 | 7.110712 | 5113.3643 | 17367.398 | 5963719581 | 141363714 | 1113239249 | 18 | 31 | 30 | 34 | 280.8409 | 8.700000 |
| Vicia cracca | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 141.873110 | 7.110712 | 13.7500 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.1077 | 10.083333 |
| Vicia hirsuta | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.500 | 52 | 104 | 18.772773 | 18.772773 | 220.224782 | 7.110712 | 3.1000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 281.6091 | 9.166667 |
| Vicia sepium | short-long | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 105.595880 | 105.595880 | 385.780391 | 3.875541 | 2.2500 | 36.000 | 8058178821 | 141363714 | 1113239249 | 18 | 26 | 25 | 29 | 280.6096 | 7.916667 |
| Vicia tenuifolia | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 56.233035 | 71.316394 | 385.780391 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 23 | 35 | 34 | 38 | 280.9342 | 8.766667 |
| Vicia tetrasperma | long-medium-short | FALSE | insects | FALSE | 0.0010 | 50 | 0.950 | 52 | 104 | 18.772773 | 18.772773 | 220.217579 | 7.110712 | 3.0250 | 112.500 | 8058178821 | 141363714 | 1113239249 | 23 | 31 | 30 | 34 | 280.3759 | 8.600000 |
| Vicia villosa | short | FALSE | insects | FALSE | 0.0010 | 50 | 0.000 | 52 | 104 | 18.772773 | 18.772773 | 149.458251 | 3.875541 | 50.0000 | 75.000 | 8058178821 | 141363714 | 1113239249 | 23 | 40 | 39 | 43 | 288.8241 | 6.783333 |
| Vincetoxicum hirundinaria | short | TRUE | insects | FALSE | 0.0010 | 50 | 0.000 | 1560 | 1560 | 105.595880 | 105.595880 | 3.875541 | 3.875541 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 18 | 35 | 34 | 38 | 280.5451 | 8.816667 |
| Viola hirta | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 14 | 22 | 21 | 25 | 279.2736 | 8.350000 |
| Viola odorata | long-medium-short | TRUE | insects | FALSE | 0.0010 | 50 | 0.050 | 1560 | 1560 | 81.167382 | 105.328987 | 147.551845 | 7.110712 | 210.0389 | 1708.098 | 8058178821 | 141363714 | 1113239249 | 9 | 17 | 16 | 20 | 282.3458 | 7.800000 |